120 accepted papers
Urban scene reconstruction under noisy camera poses remains a critical challenge for autonomous driving. While recent neural dense Bundle Adjustment (BA) methods have shown promising results in specific settings, their performance often degrades in real-world urban scenarios due to noisy corresponde
Position tracking based on bearing measurements and Ultra-wideband (UWB) ranging is widely used in robotic navigation tasks. However, due to variations in the number of robots, anchor configurations, UWB tag layouts, and the presence or absence of anonymous visual observations, existing methods typi…
We study the task of language-conditioned pick and place in clutter, where a robot should grasp a target object in open clutter and move it to a specified place. Some approaches learn end-to-end policies with features from vision foundation models, requiring large datasets. Others combine foundation…
Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen object categories remains a significant challenge. In this paper, we present a generalizable deformation learning framework that reconstructs 3D objects b…
Planning in dynamic environments often relies on explicit future observation prediction or value-based estimation, both of which can be brittle or hard to generalize in uncertain settings. We propose a novel model-based reinforcement learning framework that performs trajectory rollout and optimizati…
Soft continuum robots, attributable to inherently compliant trunks and shape manipulability, have been widely deployed in complex scenarios requiring safe human-robot interaction. However, their nonlinear deformations and hyperredundant degrees of freedom pose substantial challenges for full-body sh…
Object segmentation is a critical prerequisite for robotic tasks such as grasping and assembly. While high accuracy and reliability typically require extensive real-world data, its collection and annotation are costly. Although synthetic data generated through physically-based rendering mitigates th
Controlling symmetric objects is an indispensable but challenging task in robotic manipulation. Mainstream perception-action frameworks rely on accurate 6D pose estimation to guide the controller. However, the majority of existing 6D pose estimation methods for symmetric objects are designed to outp…
Vision-language-action policies learn manipulation skills across tasks, environments and embodiments through large-scale pre-training. However, their ability to generalize to novel robot configurations remains limited. Most approaches emphasize model size, dataset scale and diversity while paying le…
Reconstructing and decomposing dynamic urban scenes is crucial for autonomous driving, urban planning, and scene editing. However, existing methods fail to perform instance-aware decomposition without manual annotations, which is crucial for instance-level scene editing. We propose UnIRe, a 3D Gauss…
Deploying visual servo controller to novel scenes with uncertain parameters requires additional manual effort for calibration. Traditional methods tackle this problem by online estimating the Jacobian matrix. However, they struggle in challenging scenes due to intrinsic limitations. For instance, im
Implicit Neural Representation (INR) has shown great potential in constructing the complex nature signal as a continuous implicit function. However, the representation results are incomplete since different components of the signal correspond to different frequencies and neural network inherently te…
Monocular Visual Odometry (MVO) provides a cost-effective, real-time positioning solution for autonomous vehicles. However, MVO systems face the common issue of lacking inherent scale information from monocular cameras. Traditional methods have good interpretability but can only obtain relative scal
Visual servo based on traditional image matching methods often requires accurate keypoint correspondence for high precision control. However, keypoint detection or matching tends to fail in challenging scenarios with inconsistent illuminations or textureless objects, resulting significant performanc
Trajectory planning is vital for autonomous driving, ensuring safe and efficient navigation in complex environments. While recent learning-based methods, particularly reinforcement learning (RL), have shown promise in specific scenarios, RL planners struggle with training inefficiencies and managing…
Task-oriented dexterous grasp generation aims to generate stable and functional grasps that enable a robotic hand to effectively interact with objects to accomplish specific tasks. However, generating high-dimensional hand configurations that seamlessly adapt to diverse task requirements and object
Recovering 3D geometry and textures of individual objects is crucial for many robotics applications, such as manipulation, pose estimation, and autonomous driving. However, decomposing a target object from a complex background is challenging. Most existing approaches rely on costly manual labels to
Inspired by biological tentacles, soft continuum robots exhibit the potential for navigating through narrow spaces and operating in complex environments, offering extensive application possibilities. However, owing to their inherent compliance, soft continuum robots may undergo unpredictable deforma
Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning policies for these tasks. Data-driven solutions face the challenge of high real-world data costs, while simulation data
Grounding 3D object affordance is a task that locates objects in 3D space where they can be manipulated, which links perception and action for embodied intelligence. For example, for an intelligent robot, it is necessary to accurately ground the affordance of an object and grasp it according to huma…
The complex nonlinear dynamics of hydraulic excavators, such as time delays and control coupling, pose significant challenges to achieving high-precision trajectory tracking. Traditional control methods often fall short in such applications due to their inability to effectively handle these nonlinea
Nowadays, laparoscopic surgery procedures face a trade-off between expensive, complex robotic systems and manual instruments with limited functionality. Fully robotic solutions offer precision but lack portability and intuitive control, while manual tools rely solely on the surgeon’s dexterity, limi
Large-scale 3D reconstruction is critical in the field of robotics, and the potential of 3D Gaussian Splatting (3DGS) for achieving accurate object-level reconstruction has been demonstrated. However, ensuring geometric accuracy in outdoor and unbounded scenes remains a significant challenge. This s
Ultra-wideband (UWB)-vision fusion localization has achieved extensive applications in the domain of multiagent relative localization. The challenging matching problem between robots and visual detection renders existing methods highly dependent on identity-encoded hardware or delicate tuning algori
Embodied navigation in unknown environments presents the significant challenge of integrating tasks with multimodal goals into a unified framework. In this paper, we propose the Multimodal Semantic Navigation on Relative Metric Intention Graph (Ms. NAMI), a framework that integrates various navigati
Natural and lifelike locomotion remains a fundamental challenge for humanoid robots to interact with human society. However, previous methods either neglect motion naturalness or rely on unstable and ambiguous style rewards. In this paper, we propose a novel Generative Motion Prior (GMP) that provid
Ultra-wideband (UWB) has shown promising potential in GPS-denied localization thanks to its lightweight and drift-free characteristics, while the accuracy is limited in real scenarios due to its sensitivity to sensor arrangement and non-Gaussian pattern induced by multi-path or multi-signal interfer
Open-vocabulary panoptic reconstruction is a challenging task for simultaneous scene reconstruction and understanding. Recently, methods have been proposed for 3D scene understanding based on Gaussian splatting. However, these methods are multi-staged, suffering from the accumulated errors and the d
Recent advances in robotics have underscored the critical role of colorized point clouds in enhancing environmental perception accuracy. However, conventional multisensor fusion Simultaneous Localization and Mapping (SLAM) systems typically employ all available images indiscriminately for point clou
Automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated conside
This letter presents a sparse hierarchical LiDAR bundle adjustment method for online multi-robot collaborative simultaneous localization and mapping (C-SLAM). The motivation behind this work is that the pose graph cannot directly reflect map inconsistencies. As a result, the map divergence across mu
Supervised behavioral cloning using robot visual-action data has been widely investigated in robot manipulation. However, these methods typically require simultaneous acquisition of visual and action data, which makes them difficult to utilize unpaired visual-action datasets: e.g. videos on Internet
Legged robot proved their capability to cross complex terrain in recent research, yet the autonomy of robots on discrete terrain still needs to be enhanced since it requires a full stack framework. This paper introduces a real-time motion and foothold planning framework tailored for legged robots na…
Visual servoing (VS) is a widely used technique in industries where there are hundreds of robots, but it requires accurate camera calibration including camera intrinsic and extrinsic parameters. However, it is labour-intensive to calibrate robots one-by-one in practical use. In this paper, we propos…
Ensuring robust tracking of controllers’ movement is critical for human-robot interaction in virtual reality (VR) scenarios. This paper proposes a robust tracking algorithm based on a novel wearable ring-shaped controller equipped with an inertial measurement unit (IMU) and a light-emitting diode (L…
Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework…
Image servo is an indispensable technique in robotic applications that helps to achieve high precision positioning. The intermediate representation of image servo policy is important to sensor input abstraction and policy output guidance. Classical approaches achieve high precision but require clean…
This letter addresses the challenge of open-set instance segmentation (OSIS) which segments both known objects and unknown objects not seen in training, and thus is essential for enabling robots to safely work in the real world. Existing solutions adopt class-agnostic segmentation where all classes
Trajectory planning in highly constrained environments is crucial for robotic navigation. Classical algorithms are widely used for their interpretability, generalization, and system robustness. However, these algorithms often require parameter retuning when adapting to new scenarios. To address this
Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors. Many successful works have utilized mixture models which require identification of positive mixture components, and correspondingly fall into two main lines: pre…
Global trajectory planning is crucial for long-range formation navigation tasks of multi-robot systems in efficiency improvement and energy saving, whose main challenges are the joint space constraints of the whole team and the long-range deployment. To overcome the above difficulties, we reformulat
This letter introduces an innovative vehicle motion planning method that leverages the integration of rule-based insights to significantly improve closed-loop performance within a learning-based framework. We first employ rule-based methods to heuristically search and generate a diverse set of traje
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
Magnetic continuum robots are subject to external magnetic fields and deformed remotely, simplifying the robot’s transmission mechanism and providing it with significant potential for miniaturization and operational flexibility. However, modeling magnetic field distribution generated by permanent ma
Accurate perception of the dynamic environment is a fundamental task for autonomous driving and robot systems. This paper introduces Let Occ Flow, the first self-supervised work for joint 3D occupancy and occupancy flow prediction using only camera inputs, eliminating the need for 3D annotations. Ut…
Neural implicit representations have emerged as a promising solution for providing dense geometry in Simultaneous Localization and Mapping (SLAM). However, existing methods in this direction fall short in terms of global consistency and low latency. This paper presents NGEL-SLAM to tackle the above…
Vehicle-to-infrastructure cooperative 3D object detection (VIC3D) is a task that leverages both vehicle and roadside sensors to jointly perceive the surrounding environment. However, considering the high speed of vehicles, the real-time requirements, and the limitations of communication bandwidth, r…
This paper introduces an optimisation-based trajectory deformation and tracking algorithm for tethered differential-driven mobile robots. The motivation of this work is to generate self-entanglement-free (SEF) commands for a tethered differential-driven robot to track a path. Whilst existing path pl…
A generalised solver for the manipulator non-revisiting coverage path planning (NCPP) problem is proposed in this paper. Nonlinear manipulator kinematics and the imposition of task-specific constraints dictate that applying conventional coverage path planning (CPP) solutions based on 2D template mat…
Autonomous driving demands proficient trajectory planning to ensure safety and comfort. This letter introduces Policy-Embedded Planner (PEP), a novel framework that enhances closed-loop performance of imitation learning (IL) based planners by embedding a neural policy for sequential ego pose generat
Panoptic reconstruction is a challenging task in 3D scene understanding. However, most existing methods heavily rely on pre-trained semantic segmentation models and known 3D object bounding boxes for 3D panoptic segmentation, which is not available for in-the-wild scenes. In this paper, we propose a
Image-goal navigation in unknown environments with sensor error is of considerable difficulty for autonomous robots. In this paper, we propose a drift-resisting topo-metric graph to map the environment and localize the robot using only relative poses. The error-sharing mechanism under this represent…
Interactive point cloud segmentation has become a pivotal task for understanding 3D scenes, enabling users to guide segmentation models with simple interactions such as clicks, therefore significantly reducing the effort required to tailor models to diverse scenarios and new categories. However, in…
Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here we present a novel Semantics-aware Motion reTargeting (SMT) metho…
Lungdiseases are becoming one of the world's most serious health issues. Soft bronchoscope robots can achieve safe and controllable lung navigation, which will be crucial for the future early examination of lung diseases. However, due to the single driving method, the large size, and insufficient fl
A novel algorithm to solve the 2D k-shortest non-homotopic path planning (k-SNPP) task is proposed in this paper. The task is of practical significance as a sub-module for higherlevel planning and scheduling tasks, and is gaining increasing attention and focus in recent years. There have existed alg…
State estimation is a central component of autonomous navigation. To date, many methods presented have a disruptive potential for application, such as visual-inertial odometry (VIO), wheel and leg odometry (for short, body odometry). However, most of them are prone to fail in some challenging condit…
X-ray to CT registration is an essential technique to provide on-site guidance for clinicians and medical robots by aligning preoperative information with intraoperative images. Current methods focus on local registration with small capture ranges and necessitate a manual initial alignment before pr…
The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of bundle adjustment (BA), essential for autonomous driving. This paper presents ν-DBA, a novel framework implementing geometric dense bundle adjustment (DBA) using 3D neural implicit surfaces for map parametrizat…
Recently, several works achieve end-to-end visual servoing (VS) for robotic manipulation by replacing traditional controller with differentiable neural networks, but lose the ability to servo arbitrary desired poses. This letter proposes a differentiable architecture for arbitrary pose servoing: a h
We focus on the task of language-conditioned grasping in clutter, in which a robot is supposed to grasp the target object based on a language instruction. Previous works separately conduct visual grounding to localize the target object, and generate a grasp for that object. However, these works requ…
Multi-Agent Reinforcement Learning (MARL) has become a promising solution for constructing a multi-agent autonomous driving system (MADS) in complex and dense scenarios. But most methods consider agents acting selfishly, which leads to conflict behaviors. Some existing works incorporate the concept…
Piecewise constant curvature is a popular kinematics framework for continuum robots. Computing the model parameters from the desired end pose, known as the inverse kinematics problem, is fundamental in manipulation, tracking and planning tasks. In this paper, we propose an efficient multi-solution s…
With the increasing computing power, using data-driven approaches to co-design a robot's morphology and controller has become a promising way. However, most existing data-driven methods require training the controller for each morphology to calculate fitness, which is time-consuming. In contrast, th…
With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these meth…
LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with a large perspective differ…
In recent years, the visual-inertial-ranging (VIR) state estimator with a position-unknown UWB network has become popular. However, most existing VIR methods leverage centralized algorithms to initialize the UWB anchors, which are challenging to be applied to massive UWB networks. In this paper, we…
Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.To address the problem,the letter proposes a bidirectional correspondence prediction network with a point-wise attenti
Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the sel…
Drift-free localization is essential for autonomous vehicles. In this letter, we address the problem by proposing a filter-based framework, which integrates the visual-inertial odometry and the measurements from the pre-built map. In this framework, the transformation between the odometry frame and
LiDAR Mapping has been a long-standing problem in robotics. Recent progress in neural implicit representation has brought new opportunities to robotic mapping. In this letter, we propose the multi-volume neural feature fields, called NF-Atlas, which bridge the neural feature volumes with pose graph
Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In t
Human-robot motion retargeting is a crucial approach for fast learning motion skills. Achieving real-time retargeting demands high levels of synchronization and accuracy. Even though existing retargeting methods have swift calculation, they still cause time-delay effect on the synchronous retargetin…
A novel mechanism to derive self-entanglement-free path for tethered differential-driven robots is proposed in this work. The problem is tailored to the applications of tethered robots without an omni-directional tether re-tractor which is often encountered when an omni-directional tether retracting…
Generating photorealistic images with controllable camera pose and scene contents is essential for many applications including AR/VR and simulation. Despite the fact that rapid progress has been made in 3D-aware generative models, most existing methods focus on object-centric images and are not appl…
"This paper studies category-level object pose estimation based on a single monocular image. Recent advances in pose-aware generative models have paved the way for addressing this challenging task using analysis-by-synthesis. The idea is to sequentially update a set of latent variables,e.g., pose, s…
Accurate LiDAR-camera extrinsic calibration is a precondition for many multi-sensor systems in mobile robots. Most calibration methods rely on laborious manual operations and calibration targets. While working online, the calibration methods should be able to extract information from the environment…
One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to…
We focus on the task of object manipulation to an arbitrary goal pose, in which a robot is supposed to pick an assigned object to place at the goal position with a specific orientation. However, limited by the execution space of the manipulator with gripper, one-step picking, moving and releasing mi…
In recent years, Visual-Inertial Odometry (VIO) has achieved many significant progresses. However, VIO meth-ods suffer from localization drift over long trajectories. In this paper, we propose a First-Estimates Jacobian Visual-Inertial-Ranging Odometry (FEJ-VIRO) to reduce the localization drifts of…
Grasp detection based on deep learning has been a research hot spot in recent years. The performance of grasping detection models relies on high-quality, large-scale grasp datasets. Taking comprehensive consideration of quality, extendability, and annotation cost, metric-based simulation methodolog…
Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges resulting from the differences between humans and robots. Traditional optimization-based methods are time-consuming and rel
Monocular visual-inertial odometry (VIO) is a critical problem in robotics and autonomous driving. Traditional methods solve this problem based on filtering or optimization. While being fully interpretable, they rely on manual interference and empirical parameter tuning. On the other hand, learning-…
Safety is of great importance in multi-robot navigation problems. In this paper, we propose a control barrier function (CBF) based optimizer that ensures robot safety with both high probability and flexibility, using only sensor measurement. The optimizer takes action commands from the policy networ…
In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human's behavior, we design architectures with position and orientation estimators based on the seam representation for pose alignment, wh…
One primary difficulty preventing the visual localization for service robots is the robustness against changes, including environmental changes and perspective changes. In recent years, learning-based feature matching methods have been widely studied and effectively verified in practical application
LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recogn…
An optimal solution to the task-space tracking problem using a non-redundant manipulator is proposed. This is a recurring occurrence in automated manufacturing settings, e.g. welding, deburring, painting, or quality control inspections. Given a pre-defined path for the end-effector to follow, there
Global localization is essential for autonomous mobile systems, especially indoor applications where the GPS signal is denied. Although the appearance-based methods have been successfully applied in various localization tasks, they face various challenges such as light variation, viewpoint changing,…
Current RGB-based 6D object pose estimation methods have achieved noticeable performance on datasets and real world applications. However, predicting 6D pose from single 2D image features is susceptible to disturbance from changing of environment and textureless or resemblant object surfaces. Hence,…
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value. With the aid of gravity alignment, the degree of freedom in point cloud registration could be reduced to 4DoF, in which only the hea…
Assembly orders of components have direct influence on feasibility and efficiency of assembly process in manufacturing and are usually defined by experienced operators. To automate the assembly sequence generation process, we present a method using the idea of case-based reasoning, which can take ad…
Place recognition is indispensable for a drift-free localization system. Due to the variations of the environment, place recognition using single-modality has limitations. In this paper, we propose a bi-modal place recognition method, which can extract a compound global descriptor from the two modal…
Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a “sampling-weighting” loop. Sampling is decisive to the performance of MCL [1]. However, tr
Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the appearance change. In this letter
We aim to develop an efficient programming method for equipping service robots with the skill of performing sign language motions. This paper addresses the problem of transferring complex dual-arm sign language motions characterized by the coordination among arms and hands from human to robot, which…
We focus on the task of goal-oriented grasping, in which a robot is supposed to grasp a pre-assigned goal object in clutter and needs some pre-grasp actions such as pushes to enable stable grasps. However, in this task, the robot gets positive rewards from environment only when successfully grasping
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics. In this letter, we propose a hierarchical driving model with explicit models of
Recent studies on deep-learning-based small defection segmentation approaches are trained in specific settings and tend to be limited by fixed context. Throughout the training, the network inevitably learns the representation of the background of the training data before figuring out the defection.…
Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent…
In-flight objects capture is extremely challenging. The robot is required to complete trajectory prediction, interception position calculation and motion planning within tens of milliseconds. As in-flight uneven objects are affected by various kinds of forces, which leads to the time-varying acceler…
This work considers the optimal non-revisiting coverage tasks with a single non-redundant manipulator for the case when the object can be positioned at a predefined set of locations within the workcell. The scenario is often encountered in typical industrial settings, for instance when the object pr…
Utilizing the trained model under different conditions without data annotation is attractive for robot applications. Towards this goal, one class of methods is to translate the image style from another environment to the one on which models are trained. In this letter, we propose a weakly-paired set
Object 6D pose estimation is a fundamental task in many applications. Conventional methods solve the task by detecting and matching the keypoints, then estimating the pose. Recent efforts bringing deep learning into the problem mainly overcome the vulnerability of conventional methods to environment
Visual localization for planar moving robot is important to various indoor service robotic applications. To handle the textureless areas and frequent human activities in indoor environments, a novel robust visual localization algorithm which leverages dense correspondence and sparse depth for planar…
When robots move autonomously for long-term, varied appearance such as the transition from day to night and seasonal variation brings challenges to visual place recognition. Defining an appearance condition (e.g. a season, a kind of weather) as a domain, we consider that the desired representation f…
The crucial step for localization is to match the current observation to the map. When the two sensor modalities are significantly different, matching becomes challenging. In this paper, we present an end-to-end deep phase correlation network (DPCN) to match heterogeneous sensor measurements. In DPC
Embedded deformation graph is a widely used technique in deformable geometry and graphical problems. Although the technique has been transmitted to stereo (or RGB-D) camera based SLAM applications, it remains challenging to compromise the computational cost as the model grows. In practice, the proce…
Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in chang…
Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (…
In this paper, an approach for automatic peg-in-hole assembly is proposed. The task is divided into two main steps: searching phase and inserting phase. First, a multilayer perceptron network is designed to address the hole search problem and a hybrid force position controller is introduced to ensur…
A theoretically complete solution to the optimal Non-revisiting Coverage Path Planning (NCPP) problem of any arbitrarily-shaped object with a non-redundant manipulator is proposed in this work. Given topological graphs of surface cells corresponding to feasible and continuous manipulator configurati…
Visual localization has attracted considerable attention due to its low-cost and stable sensor, which is desired in many applications, such as autonomous driving, inspection robots and unmanned aerial vehicles. However, current visual localization methods still struggle with environmental changes ac…
Visual localization is one of the primary capabilities for mobile robots. Long-term visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we pro…
Long-term visual localization in outdoor environment is a challenging problem, especially faced with the cross-seasonal, bi-directional tasks and changing environment. In this paper we propose a novel visual inertial localization framework that localizes against the LiDAR-built map. Based on the geo…
Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outl…
The main contribution of this paper is a planar scan matching algorithm that makes use of the incident angle of a scan point as a feature to enhance the robustness to large relative transformations, particularly in orientation. A new definition of the incident angle is introduced and its consistency…
Autonomous robots enjoy a wide popularity nowadays and have been applied in many applications, such as home security, entertainment, delivery, navigation and guidance. It is vital for robots to track objects accurately in real time in these applications, so it is necessary to focus on tracking algor…
We propose a novel control framework to demonstrate a unique foot tilting maneuver based on ankle torque control for humanoid balance recovery. The framework consists of the variable impedance regulation at the center of mass of the robot based on the ankle torque control, the virtual stoppers to pr…
In robot programming by demonstration (PBD) for assembly tasks, one of the important topics is to inference the poses and spatial relations of parts during the demonstration. In this paper, we propose a world model called assembly graph (AG) to achieve this task. The model is able to represent the p…