55 accepted papers
Inertial Odometry (IO) has gained attention in quadrotor applications due to its sole reliance on inertial measurement units (IMUs), attributed to its lightweight design, low cost, and robust performance across diverse environments. However, most existing learning-based inertial odometry systems for…
Adapting vision-language models to remote sensing imagery remains challenging due to two key factors: limited semantic coverage in textual representations and insufficient adaptability of visual features. These issues are particularly significant in aerial scenes, which involve various visual appear
A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which, to the best of our knowledge, is the largest dataset specifically curated for off-road autonomous driving. ORAD-3D cove…
Deep learning has advanced vectorized road extraction in urban settings, yet off-road environments remain underexplored and challenging. A significant domain gap causes advanced models to fail in wild terrains due to two key issues: lack of large-scale vectorized datasets and structural weakness in
Autonomous drone racing has attracted increasing interest as a research topic for exploring the limits of agile flight. However, existing studies primarily focus on obstacle free racetracks, while the perception and dynamic challenges introduced by obstacles remain underexplored, often resulting in …
Most reinforcement learning (RL)-based methods for drone racing target fixed, obstacle-free tracks, leaving the generalization to unknown, cluttered environments largely unaddressed. This challenge stems from the need to balance racing speed and collision avoidance, limited feasible space causing po
Monocular visual odometry (VO) often suffers from scale ambiguity and interference from moving objects in real-world scenarios. Jointly learning optical flow and depth estimation provides a promising solution for these issues by leveraging their geometric correlation and task complementarity. In thi…
Multi-task test-time adaptation (MT-TTA) aims to adapt pre-trained models to dynamic environments during multi-task inference by leveraging unlabeled test data. This task is particularly challenging as different tasks respond divergently to distribution shifts, and mixed input streams containing bot
Motion planning in unstructured environments remains a challenging task, particularly in scenarios with dense obstacles and discontinuous freespace, due to the need to ensure both safety and real-time performance for robots. To address these challenges, this paper proposes a multimodal fusion-guided…
Autonomous drone racing in complex environments requires agile, high-speed flight while maintaining reliable obstacle avoidance. Differentiable-physics-based policy learning has recently demonstrated high sample efficiency and remarkable performance across various tasks, including agile drone flight
Navigation through narrow and irregular gaps is an essential skill in autonomous drones for applications such as inspection, search-and-rescue, and disaster response. However, traditional planning and control methods rely on explicit gap extraction and measurement, while recent end-to-end approaches
Fixed-Lag smoothing is widely employed as a backend in localization tasks. Generally, increasing the window length leads to better accuracy, but demands more computational resources. Therefore, determining an appropriate window length and whether a fixed length should be maintained throughout the lo
Keypoint detection and description are fundamental tasks for a variety of computer vision applications. Due to the limited receptive field of convolutional neural networks, most existing methods based on deep learning mainly focus on the local features, instead of taking into account the global cont
4D radar-based object detection has garnered great attention for its robustness in adverse weather conditions and capacity to deliver rich spatial information across diverse driving scenarios. Nevertheless, the sparse and noisy nature of 4D radar point clouds poses substantial challenges for effecti
LLMs have demonstrated impressive performance across various language tasks. However, the strengths of LLMs can vary due to different architectures, model sizes, areas of training data, etc. Therefore, ensemble reasoning for the strengths of different LLM experts is critical to achieving consistent
Open-domain dialogue systems aim to generate natural and engaging conversations, providing significant practical value in real applications such as social robotics and personal assistants. The advent of large language models (LLMs) has greatly advanced this field by improving context understanding a
Long-form story generation task aims to produce coherent and sufficiently lengthy text, essential for applications such as novel writingand interactive storytelling. However, existing methods, including LLMs, rely on rigid outlines or lack macro-level planning, making it difficult to achieve both co…
In the complex indoor environment, geomagnetic matching is an effective way to realize indoor positioning of mobile robots. Aiming at the problem that the application of Particle Swarm Optimization (PSO) algorithm leads to the decline of geomagnetic matching accuracy, stability and convergence speed
Collision-free flight in cluttered environments is a critical capability for autonomous quadrotors. Traditional methods often rely on detailed 3D map construction, trajectory generation, and tracking. However, this cascade pipeline can introduce accumulated errors and computational delays, limiting
Existing LLM-based medical question answering systems lack citation generation and evaluation capabilities, raising concerns about their adoption in practice. In this work, we introduce MedCite, the first end-to-end framework that facilitates the design and evaluation of LLM citations for medical ta…
Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas. Previous state-of-the-art (SOTA) methods employ multi-modal fusion of RGB images and LiDAR data. However, due to the significant increase in inference time when calculating
3D Gaussian Splatting (3DGS) has demonstrated its potential in reconstructing scenes from unposed images. However, optimization-based 3DGS methods struggle with sparse views due to limited prior knowledge. Meanwhile, feed-forward Gaussian approaches are constrained by input formats, making it challe…
The suspension system, through effective damping of vibrations and shocks, can enhance the stability of wheeled robots traversing challenging terrain. Because the suspension system decouples the rigid correspondence between terrain changes and robot vibrations, considering suspension modeling in tra
In unstructured environments with complex obstacles and obscure road boundaries, the local planner faces more severe challenges in terms of safety and real-time performance. In order to fulfill these emerging requirements, we propose a novel Timed-Elastic-Band approach for unstructured environments,…
In wild environments, motion planning for mobile robots faces the challenge of local optimal path traps due to limited sensor perception range and lack of spatial awareness. Existing approaches that avoid local optimum by designing heuristic functions or high-quality global paths in wild environment…
This paper reports the design, simulation, and experiment of a robotic leg prototype named Cury, which has the potential to achieve minimal clearance and excellent backdrivability. Inspired by human walking data, the actuator design incorporates four-bar linkages and ball screws and is further optim…
Trajectory planning is important for ground robots to achieve safe and efficient autonomous navigation in unstructured off-road environments. Most existing methods treat each terrain as a single type. However, in the real world, a ground usually consists of hybrid terrains. In this work, we propose
Accurately identifying and organizing textual content is crucial for the automation of document processing in the field of form understanding. Existing datasets, such as FUNSD and XFUND, support entity classification and relationship prediction tasks but are typically limited to local and entity-lev…
Speed planning in rugged terrain poses challenges due to various constraints, such as traverse efficiency, dynamics, safety, and smoothness. This letter introduces a framework based on Constrained Reinforcement Learning (CRL) that considers all these constraints. In addition, extracting the terrain
In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle’s surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem for semantic segmentation. This paper addresses the limitations of current few-shot semantic segmentation by exploiting
In the digital era, table structure recognition technology is a critical tool for processing and analyzing large volumes of tabular data. Previous methods primarily focus on visual aspects of table structure recovery but often fail to effectively comprehend the textual semantics within tables, parti…
We present AVS-HRL, a modular Active visual SLAM system based on hierarchical reinforcement learning. The reward function explicitly considers the efficiency of exploration and the accuracy of mapping by utilizing the internal variables of SLAM, such as feature points distribution and loop-closure s…
We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descr…
In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of the…
Semantic segmentation of LiDAR point clouds can provide assistance for precise perception in autonomous driving, but traditional segmentation methods face challenges such as unbalanced class distribution and insufficient labeling. Generalized few-shot learning has been researched on image data, but…
Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during tra…
Time-consuming performance evaluation is the bottleneck of traditional Neural Architecture Search (NAS) methods. Predictor-based NAS can speed up performance evaluation by directly predicting performance, rather than training a large number of sub-models and then validating their performance. Most p…
Neural Architecture Search (NAS) aims to automatically find optimal neural network architectures in an efficient way. Zero-Shot NAS is a promising technique that leverages proxies to predict the accuracy of candidate architectures without any training. However, we have observed that most existing pr…
Zero-shot object detection has shown its ability to overcome the problems of data scarcity and novel classes. Existing methods generally utilize static semantic vectors to classify objects and guide the network to map visual features to semantic vectors. However, the distribution of semantic vectors…
Micro- and macro-architecture search have emerged as two popular NAS paradigms recently. Existing methods leverage different search strategies for searching micro- and macro- architectures. When using architecture parameters to search for micro-structure such as normal cell and reduction cell, the a…
Soft robots have intrinsic advantages in interaction with humans or complex environments for actual applications, during which various external disturbances (e.g., external contact or collision) are inevitable. They show remarkable abilities in complicated tasks due to their easily deformable bodies
Model predictive control is a promising method in robot control tasks. How to design an effective model structure and efficient prediction framework for model predictive control is still an open challenge. To reduce the time consumption and avoid compounding-error of the multi-step prediction proces…
With the success of Transformers in the computer vision field, the automated design of vision Transformers has attracted significant attention. Recently, MetaFormer found that simple average pooling can achieve impressive performance, which naturally raises the question of how to design a search spa…
Feature-based lidar odometry methods have attracted increasing attention due to their low computational cost. However, theoretically analysis of the effect of extracted features on pose estimation is still lacked. In this paper, we propose a method of key-feature selection for lightweight lidar iner…
Coronavirus Disease 2019 (COVID-19) causes a sudden turnover to bad at some checkpoints and thus needs the intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not…
The task of multi-turn text-to-SQL semantic parsing aims to translate natural language utterances in an interaction into SQL queries in order to answer them using a database which normally contains multiple table schemas. Previous studies on this task usually utilized contextual information to enric…
Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current models, leading to image distortion and temporal inconsistency. We point out the necessity of exploring multi-freque…
Soft fluid-driven robots have great potential for safe interaction with humans, and for adaptation to complex and unpredictable environments with the compliance brought about by soft materials. However, the respective complex structure and massive use of nonlinear and viscoelastic soft materials, an
The extensive computational burden limits the usage of accurate but complex object detectors in resource-bounded scenarios. In this paper, we present a lightweight object detector, named LightDet, to address this dilemma. We design a lightweight backbone that is able to capture rich low-level featur…
With pervasive applications of medical imaging in healthcare, biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. Since manual annotation suffers limited reproducibility, arduous efforts, and excessive time, automatic segmentatio…
For autonomous driving, vehicle detection is the prerequisite for many tasks like collision avoidance and path planning. In this letter, we present a real-time three-dimensional (RT3D) vehicle detection method that utilizes pure LiDAR point cloud to predict the location, orientation, and size of veh
Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D…
Unsupervised video prediction is a very challenging task due to the complexity and diversity in natural scenes. Prior works directly predicting pixels or optical flows either have the blurring problem or require additional assumptions. We highlight that the crux for video frame prediction lies in pr…
Depth estimation from a single image is very challenging due to the inherent ambiguity of mapping a color image to a depth map. Previous work tackles this problem by exploiting various levels of features with multi-scale deep convolutional neural networks. However, most of the local geometric struct…
In previous work, a method to compensate the divergence between the distributions of natural and generated modulation spectra (MS) has been proposed for hidden Markov model (HMM) based speech synthesis. This method can alleviate the over-smoothing effect of parameter generation when Mel-cepstral coe…