125 accepted papers
In this paper, we propose **AlphaGRPO**, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without relying on external knowledge injection. Our approach unlocks the model's intrinsic…
Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on costly human annotations and lack scalability, while current self-supervised methods often confuse static and dynamic elemen…
Point forecasting for graph-structured multivariate time series is a fundamental problem, but rigorous uncertainty quantification for such predictions is still underexplored. Conformal prediction (CP) offers uncertainty estimation with a solid coverage guarantee under the exchangeability assumption,…
Although multi-modal large language models (MLLMs) have shown strong capabilities across diverse domains, their application in generating fine-grained 3D perception and prediction outputs in autonomous driving remains underexplored. In this paper, we propose DrivePI, a novel spatial-aware 4D MLLM th
Large Vision-Language Models (LVLMs) often hallucinate when visual evidence conflicts with world knowledge, i.e., in counterfactual scenarios. We propose Envision-Attend-Respond (EnAR), a training-free framework that leverages visual priors to steer the model's attention toward counterfactual elemen
As artificial intelligence (AI) is increasingly deployed across domains, ensuring fairness has become a core challenge. However, the field faces a "Tower of Babel'' dilemma: fairness metrics abound, yet their underlying philosophical assumptions often conflict, hindering unified paradigms—particular…
Physics-aware driving world model is essential for drive planning, out-of-distribution data synthesis, and closed-loop evaluation. However, existing methods often rely on a single diffusion model to directly map driving actions to videos, which makes learning difficult and leads to physically incons
Semi-supervised 3D object detection (SS3D), aiming to explore unlabeled data for boosting 3D object detectors, has emerged as an active research area in recent years. Some previous methods have shown substantial improvements by either employing heterogeneous teacher models to provide high-quality ps…
Distributed multi-agent systems are increasingly deployed in dynamic and high-stakes environments such as power grids, intelligent traffic systems, and collaborative robotics. In these systems, long-term stability, the ability to maintain coherent and safe system behavior over time, is critical but
Tool-calling text-to-image (T2I) agents can plan and execute multi-step tool chains to accomplish complex generation and editing queries. However, this capability introduces a new safety attack surface: harmful outputs may arise from tool orchestration, where individually benign steps combine into u…
With the rapid evolution of foundation models, Large Language Model (LLM) agents have demonstrated increasingly powerful tool-use capabilities. However, this proficiency introduces significant security risks, as malicious actors can manipulate agents into executing tools to generate harmful content.…
With the rapid evolution of foundation models, Large Language Model (LLM) agents have demonstrated increasingly powerful tool-use capabilities. However, this proficiency introduces significant security risks, as malicious actors can manipulate agents into executing tools to generate harmful content.…
Recently, sophisticated deep learning-based approaches have been developed for generating efficient initial guesses to accelerate the convergence of density functional theory (DFT) calculations. While the actual initial guesses are often density matrices (DM), quantities that can convert into densit…
Editable high-fidelity 4D scenes are crucial for autonomous driving, as they can be applied to end-to-end training and closed-loop simulation. However, existing reconstruction methods are primarily limited to replicating observed scenes and lack the capability for diverse weather simulation. While i
CRYSTALS-Dilithium has recently been selected as one of the next generation post-quantum signature algorithm standards. However, due to the extensive volume of data elements and the high complexity of operations, post-quantum cryptographic algorithms commonly face significant performance challenges,…
The emergence of the large language model (LLM) has shown its superiority in a wide range of disciplines, including language understanding and translation, relational logic reasoning, and even partial differential equations solving. The transformer is the pervasive backbone architecture for the foun…
Open-set 3D object retrieval (3DOR) is an emerging task aiming to retrieve 3D objects of unseen categories beyond the training set. Existing methods typically utilize all modalities (i.e., voxels, point clouds, multi-view images) and train specific backbones before fusion. However, they still strugg…
We study convex optimization problems under differential privacy (DP). With heavy-tailed gradients, existing works achieve suboptimal rates. The main obstacle is that existing gradient estimators have suboptimal tail property, resulting in a superfluous factor of d in the union bound. In this paper,…
Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the privacy of labels by flipping them randomly, and then train a model to make the output approximate the privatized label. Howe…
Large Language Models (LLMs) have demonstrated potential in Vision-and-Language Navigation (VLN) tasks, yet current applications face challenges. While LLMs excel in general conversation scenarios, they struggle with specialized navigation tasks, yielding suboptimal performance compared to specializ…
In warehousing systems, to enhance efficiency amid surging demand volumes, much attention has been placed on how to reasonably allocate tasks of delivery to robots. However, the labor of robots is still inevitably wasted to some extent. In this paper, we propose a pre-scheduling enhanced warehousing
In complex multi-robot application scenarios, particularly in dynamically adversarial, hazardous, or disaster environments, traditional cooperation paradigms face significant challenges due to unreliable or absent communication links. Achieving efficient cooperation in the absence of communication h
Soft manipulators (SMs) have shown great potential for interactive tasks in confined environments. However, avoiding obstacles of SMs may conflict with the manipulator’s configuration, the planned trajectory for tracking control, and the target position for grasping. To coordinate configuration plan
Transformer-based methods have demonstrated remarkable capabilities in 3D semantic segmentation through their powerful attention mechanisms, but the quadratic complexity limits their modeling of long-range dependencies in large-scale point clouds. While recent Mamba-based approaches offer efficient
Vision Transformers (ViTs) have been widely applied in various computer vision and vision-language tasks. To gain insights into their robustness in practical scenarios, transferable adversarial examples on ViTs have been extensively studied. A typical approach to improving adversarial transferabilit…
Transferable adversarial examples are known to cause threats in practical, black-box attack scenarios. A notable approach to improving transferability is using integrated gradients (IG), originally developed for model interpretability. In this paper, we find that existing IG-based attacks have limit…
Deep-learning-based object detection has gained widespread application in surface defect inspection, with anchor-based detectors achieving remarkable success by utilizing dense anchors to align with defects. Determining the optimal anchor configuration, i.e., sizes and aspect ratios of anchor boxes,
Recent advancements in multi-view scene reconstruction have been significant, yet existing methods face limitations when processing streams of input images. These methods either rely on time-consuming offline optimization or are restricted to shorter sequences, hindering their applicability in real-…
Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, such as mobile devices, remains challenging due to high computation and memory demands. To address these challenges and f…
Moving object segmentation plays a vital role in understanding dynamic visual environments. While existing methods rely on multi-frame image sequences to identify moving objects, single-image MOS is critical for applications like motion intention prediction and handling camera frame drops. However,
Humans navigate unfamiliar environments using episodic simulation and episodic memory, which facilitate a deeper understanding of the complex relationships between environments and objects. Developing an imaginative memory system inspired by human mechanisms can enhance the navigation performance of…
Pneumatic soft actuators are known for their versatility and reliability; however, their control presents a major challenge as systems scale beyond tens of actuators. Traditional rigid pneumatic valves add bulk, weight, and complexity, while most soft valves fail to generate programmable independent
Visual navigation is a fundamental problem in embodied AI, yet practical deployments demand long-horizon planning capabilities to address multi-objective tasks. A major bottleneck is data scarcity: policies learned from limited data often overfit and fail to generalize OOD. Existing neural network-b…
Text-to-image (T2I) models excel at generating high-quality images from text via powerful text encoders but training these encoders demands substantial computational resources. Consequently, many users seek pre-trained text encoders from model plugin-sharing platforms like Civitai and Hugging Face,…
Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process. While Evidential Deep Learning (EDL) has been effective in addressing view uncertainty, existing methods predominantly…
Depth estimation plays a crucial role in 3D scene understanding and is extensively used in a wide range of vision tasks. Image-based methods struggle in challenging scenarios, while event cameras offer high dynamic range and temporal resolution but face difficulties with sparse data. Combining event
Autonomous exploration in complex multi-agent reinforcement learning (MARL) with sparse rewards critically depends on providing agents with effective intrinsic motivation. While artificial curiosity offers a powerful self-supervised signal, it often confuses environmental stochasticity with meaningf…
Privacy protection of users' entire contribution of samples is important in distributed systems. The most effective approach is the two-stage scheme, which finds a small interval first and then gets a refined estimate by clipping samples into the interval. However, the clipping operation induces bia…
Deep clustering with attribute-missing graphs, where only a subset of nodes possesses complete attributes while those of others are missing, is an important yet challenging topic in various practical applications. It has become a prevalent learning paradigm in existing studies to perform data imputa…
Pre-trained models with large-scale training data such as CLIP and Stable Diffusion have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions. Yet their potential for low-level tasks such as image restor…
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies despite their advancements tend to neglect the distinct specificity levels present in attributes. For instance given images of sliced strawb…
We explore contextual biasing with Large Language Models (LLMs) to enhance Automatic Speech Recognition (ASR) in second-pass rescoring. Our approach introduces the utilization of prompts for LLMs during rescoring without the need for fine-tuning. These prompts incorporate a biasing list and a set of…
Recently, Shuttle-based Storage and Retrieval Systems (SBS/RSs) have garnered significant attention from both academia and industry, owing to their high spatial utilization and rapid response speed. However, the weak connectivity of roadmaps in densely stored environments increases the likelihood of…
Language models (LMs) have been commonly adopted to boost the performance of automatic speech recognition (ASR) particularly in domain adaptation tasks. Conventional way of LM training treats all the words in corpora equally, resulting in suboptimal improvements in ASR performance. In this work, we…
We propose DOC-RAG - Domain-distributed Co-occurrence Retrieval Augmentation for ASR language model personalization aiming to improve the automatic speech recognition of rare word patterns in unseen domains. Our approach involves contrastively training a document retrieval module to rank external kn…
"Information inside visual and LiDAR data is well complementary derived from the fine-grained texture of images and massive geometric information in point clouds. However, it remains challenging to explore effective visual-LiDAR fusion, mainly due to the intrinsic data structure inconsistency betwee…
In numerous real-world applications, it is quite common that sample information is partially available for some views due to machine breakdown or sensor failure, causing the problem of incomplete multi-view clustering (IMVC). While several IMVC approaches using view-shared anchors have successfully…
Scene flow estimation which aims to predict per-point 3D displacements of dynamic scenes is a fundamental task in the computer vision field. However previous works commonly suffer from unreliable correlation caused by locally constrained searching ranges and struggle with accumulated inaccuracy aris…
In the context of visual navigation in unknown scenes, both “exploration” and “exploitation” are equally crucial. Robots must first establish environmental cognition through exploration and then utilize the cognitive information to accomplish target searches. However, most existing methods for image
Recent research has shown that language models have a tendency to memorize rare or unique token sequences in the training corpus. After deploying a model, practitioners might be asked to delete any personal information from the model by individuals’ requests. Re-training the underlying model every t…
"We present a novel graph memory structure for navigation, called Frontier-enhanced Topological Memory (FTM). Most prior research primarily focuses on maintaining memory representations for explored areas. In contrast, our approach incorporates ghost nodes into the topological map to characterize un…
Crime prediction is a crucial yet challenging task within urban computing, which benefits public safety and resource optimization. Over the years, various models have been proposed, and spatial-temporal hypergraph learning models have recently shown outstanding performances. However, three correlati…
The benefit of transformers in large-scale 3D point cloud perception tasks, such as 3D object detection, is limited by their quadratic computation cost when modeling long-range relationships. In contrast, linear RNNs have low computational complexity and are suitable for long-range modeling. Toward…
Goal-reaching in unknown environments is one of the essential tasks in robot applications. Large-scale perception and long-horizon decision-making are the keys to solving this task as the operation scope expands or complexity rises. Existing navigation methods may suffer from degraded performance in
"Accurate depth information is crucial for enhancing the performance of multi-view 3D object detection. Despite the success of some existing multi-view 3D detectors utilizing pixel-wise depth supervision, they overlook two significant phenomena: 1) the depth supervision obtained from LiDAR points is…
Three-dimensional object detection is one of the key tasks in autonomous driving. To reduce costs in practice low-cost multi-view cameras for 3D object detection are proposed to replace the expansive LiDAR sensors. However relying solely on cameras is difficult to achieve highly accurate and robust…
Model adaptation is crucial to handle the discrepancy between proxy training data and actual users’ data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly…
"Recently, detection transformers (DETRs) have gradually taken a dominant position in 2D detection thanks to their elegant framework. However, DETR-based detectors for 3D point clouds are still difficult to achieve satisfactory performance. We argue that the main challenges are twofold: 1) How to ob…
To date, multi-robot path planning has primarily been addressed by centralized solvers, typically aiming to maintain optimality. However, given its NP-hard nature, directly applying existing solvers in large and complex scenarios proves inefficient. A promising alternative lies in adopting a divide-
Robotic systems with hundreds or even thousands of robots are widely implemented in logistic and industrial applications. In such systems, cooperative path planning is of great importance, as local congestion and motion conflict may greatly degrade system performance, especially in the presence of u…
Advanced 3D object detection methods usually rely on large-scale, elaborately labeled datasets to achieve good performance. However, labeling the bounding boxes for the 3D objects is difficult and expensive. Although semi-supervised (SS3D) and weakly-supervised 3D object detection (WS3D) methods can…
Neural network language model (NNLM) plays an essential role in automatic speech recognition (ASR) systems, especially in adaptation tasks when text-only data is available. In practice, an NNLM is typically trained on a combination of data sampled from multiple corpora. Thus, the data sampling strat…
Human drivers are remarkably robust against various unexpected occurring variations and corruptions by understanding temporal changes and traffic scenes. In contrast, the neural network based autonomous navigation system can be easily affected by sensor data anomaly, like occlusion, sensor noise, ch…
Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factori…
Consistency plays an important role in learning theory. However, in multiple kernel clustering (MKC), the consistency of kernel weights has not been sufficiently investigated. In this work, we fill this gap with a non-asymptotic analysis on the consistency of kernel weights of a novel method termed…
In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-…
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world setting degrade severely due to the unconstrained OW test space. While previous works alleviate the issue by pruning com…
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly…
Federated learning (FL) can help promote data privacy by training a shared model in a de-centralized manner on the physical devices of clients. In the presence of heterogeneous distributions of local data, personalized FL strategy is introduced to mitigate the potential client drift. In this paper,…
To solve the problem of inaccurate orientation estimation after long-term operations of the Inertial Measurement Unit (IMU), we present a learning-based method (called Gyro-Net) to estimate and compensate for IMU gyroscope random errors. We firstly introduce a semi-dense network structure, which ext
Event cameras have the potential to overcome the limitations of classical computer vision in real-world applications. Depth estimation is a crucial step for high-level robotics tasks and has attracted much attention from the community. In this paper, we propose an event-based dense depth estimation…
In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance the clustering performance by exploring information from multiple views. Despite the superior performance, the high time and space expenditures limit their scalability. Accordingly, anchor graph lear…
Traditional methods based on Lyapunov analysis and learning-based approaches such as reinforcement learning (RL) are two powerful tools in visual servo tasks. Traditional methods are interpretable and their stability can be guar-anteed by Lyapunov analysis. However, they tend to have a high dependen…
Recent research has shown that language models have a tendency to memorize rare or unique sequences in the training corpora which can thus leak sensitive attributes of user data. We employ a teacher-student framework and propose a novel approach called alternating teaching to mitigate unintended mem…
Improving the transferability of adversarial examples for the purpose of attacking unknown black-box models has been intensively studied. In particular, feature-level transfer-based attacks, which destroy the intermediate feature outputs of source models, are proven to generate more transferable adv…
We introduce PersonaLM - Domain-distributed Span-Aggregated K-nearest N-gram retrieval augmentation to improve language modeling for Automatic Speech Recognition (ASR) personalization. PersonaLM leverages contextually similar n-gram word frequencies for recognizing rare word patterns associated with…
Effectively utilizing temporal information to improve 3D detection performance is vital for autonomous driving vehicles. Existing methods either conduct temporal fusion based on the dense BEV features or sparse 3D proposal features. However, the former does not pay more attention to foreground objec…
Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effe…
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing regist…
We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have access to global positioning information and only use first-person- view images. In order to overcome the need for positio…
Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are independent of each other. They only use the adjacent image frames for pose estimation and…
In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors from the superior LiDAR model at the response level, which is usually overlooked in 3D object detection distillation. Th…
Recently, transformer architecture has gained great success in the computer vision community, such as image classification, object detection, etc. Nonetheless, its application for 3D vision remains to be explored, given that point cloud is inherently sparse, irregular, and unordered. Furthermore, ex…
Monocular 3D Object Detection is an essential task for autonomous driving. Meanwhile, accurate 3D object detection from pure images is very challenging due to the loss of depth information. Most existing image-based methods infer objects' location in 3D space based on their 2D sizes on the image pla
3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leveraging 3D networks results in huge computational costs, which are far beyond that of 2D networks. In this paper, we propose…
The issue of fairness arises when the automatic speech recognition (ASR) systems do not perform equally well for all subgroups of the population. In any fairness measurement studies for ASR, the open questions of how to control the confounding factors, how to handle unobserved heterogeneity across s…
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transducers (FSTs) in a mathematically consistent framework. Our method utilizes a background NNLM which models generic backgroun…
In recent years, self-supervised paradigms for depth estimation have drawn lots of attention from the community. Promising as they are, in order to achieve wider application and better performance, reasoning about the uncertainty of the estimated depth maps is in great demand. In this paper, we prop
"3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it encodes the point motion between two consecutive frames. Thus, it is critical for the flow embeddings to capture the c…
In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to o…
Stiffness variation and real-time position feedback are critical for any robotic system but most importantly for active and wearable devices to interact with the user and environment. Currently, for compact sizes, there is a lack of solutions bringing high-fidelity feedback and maintaining design an
We introduce federated marginal personalization (FMP), a novel method for continuously updating personalized neural network language models (NNLMs) on private devices using federated learning (FL). Instead of fine-tuning the parameters of NNLMs on personal data, FMP regularly estimates global and pe…
Existing vanishing point (VP) estimation methods rely on pre-extracted image lines and/or prior knowledge of the number of VPs. However, in practice, this information may be insufficient or unavailable. To solve this problem, we propose a network that treats a perspective image as input and predicts…
Given a set of putative 2D-2D line correspondences, we aim to identify correct matches. Existing methods exploit the geometric constraints. They are only applicable to structured scenes with orthogonality, parallelism and coplanarity. In contrast, we propose the first approach suitable for both stru…
The domains of transport and logistics are increasingly relying on autonomous mobile robots for the handling and distribution of passengers or resources. At large system scales, finding decentralized path planning and coordination solutions is key to efficient system performance. Recently, Graph Neu
Soft robotic gloves have shown great advantages in assisting individuals with hand pathologies to perform continuous exercises to restore their hand functions, which could considerably accelerate the rehabilitation process and reduce the costs. However, single rehabilitation mode, difficulty in achi…
A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse…
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer the correlation between visual features and attributes to train the classifier for unseen classes. They may have a strong bi…
This letter studies the task-space synchronization control problem of networked manipulators which are commanded to track the desired task-space trajectories to achieve the coordinated manipulation transportation tasks in industrial and logistic applications. To guarantee the practical applicability
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment. Once deployed, emergent errors can be hard to identify in predi…
Cooperative adaptive cruise control (CACC) of intelligent vehicles contributes to improving cruise control performance, reducing traffic congestion, saving energy and increasing traffic flow capacity. In this paper, we resolve the CACC problem from the viewpoint of synchronization control, our main…
We explore two adaptation approaches of deep Transformer based neural language models (LMs) for automatic speech recognition. The first approach is a pretrain-finetune framework, where we first pretrain a Transformer LM on a large-scale text corpus from scratch and then adapt it to relatively small…
This paper presents a novel dataset targeting three types of challenging environments for autonomous driving, i.e., the industrial logistics environment, the undulating hill environment and the mixed complex urban environment. To the best of the author’s knowledge, similar dataset has not been publi…
In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors (namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification confidence. To this end, we propose a novel fusion mo…
3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information. In this paper, we present an end-to-end deep-learn…
Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE. However, traditional GCN simply…
Atlanta world holds for the scenes composed of a vertical dominant direction and several horizontal dominant directions. Vanishing point (VP) is the intersection of the image lines projected from parallel 3D lines. In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-b…
Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solution
This paper presents a novel solution for online trajectory planning of a full-size tractor-trailers vehicle composed of a car-like tractor and arbitrary number of passive full trailers. The motion planning problem for such systems was rarely addressed due to the complex nonlinear dynamics. A simulat…
In this paper, we propose a dynamic state estimation framework for lateral control of a heavy tractor-trailers system using only mass-produced low-cost sensors. This issue is challenging since the lateral velocity of the lead tractor is difficult to measure directly. The performance of existing dyna…
This letter reports a general path following framework for the tractor-trailers system in Global Positioning System (GPS)-denied environments. Compared to existing methods, this approach prioritizes a robust, cost-optimized, and easy-to-implement solution. First, to achieve accurate path following,
Given a set of 3D-to-2D point correspondences corrupted by outliers, we aim to robustly estimate the absolute camera pose. Existing methods robust to outliers either fail to guarantee high robustness and efficiency simultaneously, or require an appropriate initial pose and thus lack generality. In c…
In this paper, we study the cooperative path planning and motion coordination problems of the multi-robot system with large number of robots, aiming for practical applications in robotic warehouses and automated transportation systems. Particularly, we solve the life-long planning problem and guaran…
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments. In this paper, we develop a novel deep neural network, named L…
A wide range of man-made environments can be abstracted as the Atlanta world. It consists of a set of Atlanta frames with a common vertical (gravitational) axis and multiple horizontal axes orthogonal to this vertical axis. This paper focuses on leveraging the regularity of Atlanta world for monocul…
Existing works on control of tractor-trailers systems only consider the kinematics model without taking dynamics into account. Also, most of them treat the issue as a pure control theory problem whose solutions are difficult to implement. This paper presents a trajectory tracking control approach fo…
The image lines projected from parallel 3D lines intersect at a common point called the vanishing point (VP). Manhattan world holds for the scenes with three orthogonal VPs. In Manhattan world, given several lines in a calibrated image, we aim at clustering them by three unknown-but-sought VPs. The…
Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query images. However, in environments with drastically varying conditions (e.g. illumination changes, seasons, occlusion, dy…
Place recognition and loop-closure detection are main challenges in the localization, mapping and navigation tasks of self-driving vehicles. In this paper, we solve the loop-closure detection problem by incorporating the deep-learning based point cloud description method and the coarse-to-fine seque…
Most existing controllers for Car-Like Mobile Robots (CLMR) are designed to handle dynamic effects by decoupling speed and steering controls, also assume that full states are accessible, which are unrealistic for real-world applications. This paper presents a combined speed and steering control syst…
Robot malfunction is inevitable in practical applications of the robot formation control due to uncontrolled crashing, system malfunction or communication loss. In this paper, we study the synchronous formation control problem in the presence of robot malfunctions. Our main idea is to improve the ne…
Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition…
Most existing trajectory tracking controllers are based on non-skidding and non-slipping assumptions, also assume that full states are accessible, which is unrealistic for real-world applications due to tire-road interaction. This paper presents a novel vision-based approach to achieve high performa…
We present a framework for incorporating prior information into nonparametric estimation of graphical models. To avoid distributional assumptions, we restrict the graph to be a forest and build on the work of forest density estimation (FDE). We reformulate the FDE approach from a Bayesian perspectiv…
In this paper, we investigate the self-healing problem of mobile robot formation after some robots have been damaged, and present a gradient-based algorithm which enables mobile robots to restore the topology of the formation through local interactions among neighboring robots. Firstly, in order to…