Publications

2025

1. T-RO

   

   Occupancy-SLAM: An Efficient and Robust Algorithm for Simultaneously Optimizing Robot Poses and Occupancy Map

   Yingyu Wang, Liang Zhao, and Shoudong Huang

   IEEE Transactions on Robotics (T-RO), 2025

   Abs arXiv Video Code

   Joint optimization of poses and features has been extensively studied and demonstrated to yield more accurate results in feature-based SLAM problems. However, research on jointly optimizing poses and non-feature-based maps remains limited. Occupancy maps are widely used non-feature-based environment representations because they effectively classify spaces into obstacles, free areas, and unknown regions, providing robots with spatial information for various tasks. In this paper, we propose Occupancy-SLAM, a novel optimization-based SLAM method that enables the joint optimization of robot trajectory and the occupancy map through a parameterized map representation. The key novelty lies in optimizing both robot poses and occupancy values at different cell vertices simultaneously, a significant departure from existing methods where the robot poses need to be optimized first before the map can be estimated. This paper focuses on 2D laser-based SLAM to investigate how to jointly optimize robot poses and the occupancy map. In our formulation, the state variables in optimization include all the robot poses and the occupancy values at discrete cell vertices in the occupancy map. Moreover, a multi-resolution optimization framework that utilizes occupancy maps with varying resolutions in different stages is introduced. A variation of Gauss-Newton method is proposed to solve the optimization problem at different stages to obtain the optimized occupancy map and robot trajectory. The proposed algorithm is efficient and converges easily with initialization from either odometry inputs or scan matching, even when only limited key-frame scans are used. Furthermore, we propose an occupancy submap joining method, enabling more effective handling of large-scale problems by incorporating the submap joining process into the Occupancy-SLAM framework. Evaluations using simulations and practical 2D laser datasets demonstrate that the proposed approach can robustly obtain more accurate robot trajectories and occupancy maps than state-of-the-art techniques with comparable computational time. Preliminary results in the 3D case further confirm the potential of the proposed method in practical 3D applications, achieving more accurate results than existing methods.

2. IROS

   

   Correspondence-Free Multiview Point Cloud Registration via Depth-Guided Joint Optimisation

   Yiran Zhou, Yingyu Wang, Shoudong Huang, and Liang Zhao

   In IEEE/RSJ International Conference on Intelligent Robots and Systems , 2025

   Abs PDF Video

   Multiview point cloud registration is a fundamental task for constructing globally consistent 3D models. Existing approaches typically rely on feature extraction and data association across multiple point clouds; however, these processes are challenging to obtain global optimal solution in complex environments. In this paper, we introduce a novel correspondence-free multiview point cloud registration method. Specifically, we represent the global map as a depth map and leverage raw depth information to formulate a non-linear least squares optimisation that jointly estimates poses of point clouds and the global map. Unlike traditional feature-based bundle adjustment methods, which rely on explicit feature extraction and data association, our method bypasses these challenges by associating multi-frame point clouds with a global depth map through their corresponding poses. This data association is implicitly incorporated and dynamically refined during the optimisation process. Extensive evaluations on real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy, particularly in challenging environments where feature extraction and data association are difficult.

2024

1. IROS

   

   Grid-based Submap Joining: An Efficient Algorithm for Simultaneously Optimizing Global Occupancy Map and Local Submap Frames

   Yingyu Wang, Liang Zhao, and Shoudong Huang

   In IEEE/RSJ International Conference on Intelligent Robots and Systems , 2024

   Abs arXiv Video

   In this paper, we first formulate the 2D grid-based submap joining problem as a non-linear least squares (NLLS) form to optimize the global occupancy map and local submap frames simultaneously. We then prove that in solving the NLLS problem using Gauss-Newton (GN) method, the increments of the poses in each iteration are independent of the occupancy values of the global occupancy map. Based on this property, we propose a pose-only GN algorithm equivalent to full GN method to solve the NLLS problem. The proposed submap joining algorithm is very efficient due to the independent property and the pose-only solution. Evaluations using simulations and publicly available practical 2D laser datasets confirm the outperformance of our proposed method compared to the state-of-the-art methods in terms of efficiency and accuracy, as well as the ability to solve the grid-based SLAM problem in very large-scale environments.

2022

1. RSS

   

   Occupancy-SLAM: Simultaneously Optimizing Robot Poses and Continuous Occupancy Map

   Liang Zhao, Yingyu Wang, and Shoudong Huang

   In Proceedings of Robotics: Science and Systems , Jun 2022

   Abs arXiv Video Code

   In this paper, we propose an optimization based SLAM approach to simultaneously optimize the robot trajectory and the occupancy map using 2D laser scans (and odometry) information. The key novelty is that the robot poses and the occupancy map are optimized together, which is significantly different from existing occupancy mapping strategies where the robot poses need to be obtained first before the map can be estimated. In our formulation, the map is represented as a continuous occupancy map where each 2D point in the environment has a corresponding evidence value. The Occupancy-SLAM problem is formulated as an optimization problem where the variables include all the robot poses and the occupancy values at the selected discrete grid cell nodes. We propose a variation of Gauss-Newton method to solve this new formulated problem, obtaining the optimized occupancy map and robot trajectory together with their uncertainties. Our algorithm is an offline approach since it is based on batch optimization and the number of variables involved is large. Evaluations using simulations and publicly available practical 2D laser datasets demonstrate that the proposed approach can estimate the maps and robot trajectories more accurately than the state-of-the-art techniques, when a relatively accurate initial guess is provided to our algorithm.

2021

1. Incorporating lambertian priors into surface normals measurement

   Yakun Ju, Muwei Jian, Shaoxiang Guo, Yingyu Wang, Huiyu Zhou, and Junyu Dong

   IEEE Transactions on Instrumentation and Measurement, Jun 2021

2020

1. Self-supervised depth completion with attention-based loss

   Yingyu Wang, Yakun Ju, Muwei Jian, Kin-Man Lam, Lin Qia, and Junyu Dong

   In International Workshop on Advanced Imaging Technology (IWAIT) 2020 , Jun 2020
2. A joint guidance-enhanced perceptual encoder and atrous separable pyramid-convolutions for image inpainting

   Yongle Zhang, Yingyu Wang, Junyu Dong, Lin Qi, Hao Fan, Xinghui Dong, Muwei Jian, and Hui Yu

   Neurocomputing, Jun 2020
3. A dual-cue network for multispectral photometric stereo

   Yakun Ju, Xinghui Dong, Yingyu Wang, Lin Qi, and Junyu Dong

   Pattern Recognition, Jun 2020