77 citations · 144 across the 39 of their papers we have counts for
13 papers · 1 filter
Selective Kalman Filter: When and How to Fuse Multi-Sensor Information to Overcome Degeneracy in SLAM
Jie Xu, Guanyu Huang, Wenlu Yu +5
Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM me…
UA-MPC: Uncertainty-Aware Model Predictive Control for Motorized LiDAR Odometry
Jianping Li, Xinhang Xu, Jinxin Liu +3
Accurate and comprehensive 3D sensing using LiDAR systems is crucial for various applications in photogrammetry and robotics, including facility inspection, Building Information Mo…
Large-Scale UWB Anchor Calibration and One-Shot Localization Using Gaussian Process
Shenghai Yuan, Boyang Lou, Thien-Minh Nguyen +7
Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seap…
Swept Volume-Aware Trajectory Planning and MPC Tracking for Multi-Axle Swerve-Drive AMRs
Tianxin Hu, Shenghai Yuan, Ruofei Bai +4
Multi-axle autonomous mobile robots (AMRs) are set to revolutionize the future of robotics in logistics. As the backbone of next-generation solutions, these robots face a critical…
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information
Kuan Xu, Zeyu Jiang, Haozhi Cao +3
Scene Coordinate Regression (SCR) is a visual localization technique that utilizes deep neural networks (DNN) to directly regress 2D-3D correspondences for camera pose estimation.…
Graph Optimality-Aware Stochastic LiDAR Bundle Adjustment with Progressive Spatial Smoothing
Jianping Li, Thien-Minh Nguyen, Muqing Cao +3
Large-scale LiDAR Bundle Adjustment (LBA) to refine sensor orientation and point cloud accuracy simultaneously to build the navigation map is a fundamental task in logistics and ro…
SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment
Xingyu Ji, Shenghai Yuan, Jianping Li +3
LiDAR bundle adjustment (BA) is an effective approach to reduce the drifts in pose estimation from the front-end. Existing works on LiDAR BA usually rely on predefined geometric fe…
GERA: Geometric Embedding for Efficient Point Registration Analysis
Geng Li, Haozhi Cao, Mingyang Liu +2
Point cloud registration aims to provide estimated transformations to align point clouds, which plays a crucial role in pose estimation of various navigation systems, such as surgi…
AIR-Embodied: An Efficient Active 3DGS-based Interaction and Reconstruction Framework with Embodied Large Language Model
Zhenghao Qi, Shenghai Yuan, Fen Liu +4
Recent advancements in 3D reconstruction and neural rendering have enhanced the creation of high-quality digital assets, yet existing methods struggle to generalize across varying…
Distance-based Multiple Non-cooperative Ground Target Encirclement for Complex Environments
Fen Liu, Shenghai Yuan, Kun Cao +2
This paper proposes a comprehensive strategy for complex multi-target-multi-drone encirclement in an obstacle-rich and GPS-denied environment, motivated by practical scenarios such…
AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System
Kuan Xu, Yuefan Hao, Shenghai Yuan +2
In this paper, we present an efficient visual SLAM system designed to tackle both short-term and long-term illumination challenges. Our system adopts a hybrid approach that combine…
Salient Sparse Visual Odometry With Pose-Only Supervision
Siyu Chen, Kangcheng Liu, Chen Wang +3
Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs. While traditional VO methods exce…
Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes
Tianchen Deng, Nailin Wang, Chongdi Wang +5
Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in larg…