41 accepted papers
Quadruped robots have received increasing attention in recent years. Most existing trajectory planning algorithms for quadruped robots focus on how to avoid obstacles and achieve shortest trajectory or time, which is similar to the planning algorithms for mobile robots. These algorithms could not ta
Quadruped robots have received increasing attention in recent years. Most existing trajectory planning algorithms for quadruped robots focus on how to avoid obstacles and achieve shortest trajectory or time, which is similar to the planning algorithms for mobile robots. These algorithms could not ta…
End-to-end autonomous driving has been greatly advanced in recent years. However, most of existing work focuses on small vehicles (e.g., cars). Driving articulated trucks, such as tractor-trailers, still remains less being explored. The underactuated nature and extended wheelbase of tractor-trailers…
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated impressive progress in high-fidelity scene reconstruction within visual SLAM. However, existing approaches often suffer from scene inconsistency, leading to visual artifacts, and the explicit maintenance of millions of Gaussians impos…
Semantic scene understanding in bird-eye view (BEV) plays a crucial role in autonomous driving. A common approach to generating BEV maps from LiDAR point-cloud data involves constructing a pillar-level representation by projecting 3D point clouds onto a 2D plane. This process partially discards spat
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to navigate 3D environments based on visual observations and natural language instructions. Existing approaches, focused on topological and semantic maps, often face limitations in accurately understanding and adaptin
This study presents a system, TripletLoc, for fast and robust global registration of a single LiDAR scan to a large-scale reference map. In contrast to conventional methods using place recognition and point cloud registration, TripletLoc directly generates correspondences on lightweight semantics, w
A variety of Neural Radiance Fields (NeRF) methods have recently achieved remarkable success in high render speed. However, current accelerating methods are specialized and incompatible with various implicit methods, preventing real-time composition over various types of NeRF works. Because NeRF rel…
Semantic scene understanding is a fundamental capability for autonomous vehicles. Under challenging lighting conditions, such as nighttime and on-coming headlights, the semantic scene understanding performance using only RGB images are usually degraded. Thermal images can provide complementary infor
Global metric localization is one of the fundamental capabilities for autonomous vehicles. Most existing methods rely on global navigation satellite systems (GNSS). Some methods relieve the need of GNSS by using 3-D LiDARs. They first achieve place recognition with a pre-built geo-referenced point-c
Pose graph optimization (PGO) is a fundamental technique for robot localization. It is typically encoded with a sparse graph. The recent work on the cycle-based PGO reveals the merits of solving PGOs in the graph cycle space, which brings the computation of the minimum cycle basis (MCB) into the rob
Semantic scene understanding using thermal images has received great attention due to the advantage that thermal imaging cameras could see in challenging illumination conditions. However, thermal images are lack of color information and the edges in thermal images are often blurred, making them not
With the fast development of autonomous driving technologies, there is an increasing demand for high-definition (HD) maps, which provide reliable and robust prior information about the static part of the traffic environments. As one of the important elements in HD maps, road lane centerline is criti…
Dense depth estimation is an important task for applications, such as object detection, 3-D reconstruction, etc. Stereo matching, as a popular method for dense depth estimation, has been faced with challenges when low textures, occlusions or domain gaps exist. Stereo-LiDAR fusion has recently become
Segmentation of road obstacles, including negative and positive obstacles, is critical to the safe navigation of autonomous vehicles. Recent methods have witnessed an increasing interest in using multi-modal data fusion (e.g., RGB and depth/disparity images). Although improved segmentation accuracy
The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are usually annotated by human experts manually, which is time-consuming and labor-intensive. To annotate road network graph
Most current LiDAR simultaneous localization and mapping (SLAM) systems build maps in point clouds, which are sparse when zoomed in, even though they seem dense to human eyes. Dense maps are essential for robotic applications, such as map-based navigation. Due to the low memory cost, mesh has become…
Semantic bird-eye-view (BEV) grid map is a straightforward data representation for semantic environment perception. It can be conveniently integrated with downstream tasks, such as motion planning, trajectory prediction, etc. Most existing methods of semantic BEV grid-map generation adopt supervised
Assistive robots for healthcare have witnessed a growing demand over the past decades. In this letter, we investigate the development of an optimization-based control framework with variable impedance for an assistive robot to perform ultrasound-guided scoliosis assessment. The conventional procedur
Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performance based on previously trained models in different geographical regions. This letter addresses the problem of their depl
High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one important information presented in HD maps since it distinguishes between road areas and off-road areas, which can guide vehicles to drive with
Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. Most of the current methods detect road curbs online using vehicle-mounted sensors, such as cameras or 3-D Lidars. Howeve…
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable cases. Recently, self-driving methods based on deep learning have shown pro…
Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretability, making the behaviors of the deep networks difficult to explain. Hence, their potential applications could be limited…
Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise registration and pose synchronization, the concept of Bundle Adjustment (BA) has been proven to better maintain global c
Moving-obstacle segmentation is an essential capability for autonomous driving. For example, it can serve as a fundamental component for motion planning in dynamic traffic environments. Most of the current 3-D Lidar-based methods use road segmentation to find obstacles, and then employ ego-motion co
Path planning is a fundamental capability for autonomous navigation of robotic wheelchairs. With the impressive development of deep-learning technologies, imitation learning-based path planning approaches have achieved effective results in recent years. However, the disadvantages of these approaches…
Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detection using aerial images could alleviate the severe
Detection of road curbs is an essential capability for autonomous driving. It can be used for autonomous vehicles to determine drivable areas on roads. Usually, road curbs are detected on-line using vehicle-mounted sensors, such as video cameras and 3-D Lidars. However, on-line detection using video
The joint detection of drivable areas and road anomalies is a crucial task for ground mobile robots. In recent years, many impressive semantic segmentation networks, which can be used for pixel-level drivable area and road anomaly detection, have been developed. However, the detection accuracy still…
Incorporating prior structure information into the visual state estimation could generally improve the localization performance. In this letter, we aim to address the paradox between accuracy and efficiency in coupling visual factors with structure constraints. To this end, we present a cross-modali
Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled precisely. We propose a robust drift controller without explicit motion equations, whi
Robustness and accuracy for monocular visual odometry (VO) under challenging environments are widely concerned. In this paper, we present a monocular VO system leveraging learned repeatability and description. In a hybrid scheme, the camera pose is initially tracked on the predicted repeatability ma…
Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion con
All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors. Recently, with the rise of deep learning, end-to-end control for autonomous vehicles has been well studied. However, most
Ego-vehicle trajectory prediction is important for autonomous vehicles to detect collisions and accordingly avoid accidents. Recent approaches employ prior-known or on-line acquired road topology or geometries as motion constraints for their predictive models. However, the prior-known information (e
Due to the enormous volume of point cloud data, transmitting and storing the data requires large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such as autonomous driving. In this letter, we propose a novel point cloud compression algorithm based on clustering. T
Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for…
Semantic segmentation is a fundamental capability for autonomous vehicles. With the advancements of deep learning technologies, many effective semantic segmentation networks have been proposed in recent years. However, most of them are designed using RGB images from visible cameras. The quality of R
The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentation using deep learning techniques has presented effective results. However, the acquisition of large-scale datasets with
Powered lower-limb orthoses and prostheses are attracting an increasing amount of attention in assisting daily living activities. To safely and naturally collaborate with human users, the key technology relies on an intelligent controller to accurately decode users' movement intention. In this work,…