Publications

2022

1. IROS

   

   Active SLAM in 3D deformable environments

   Mengya Xu, Liang Zhao, Shoudong Huang, and Qi Hao

   In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022

   Abs DOI Code

   This paper considers active SLAM problem for 3D deformable environments where the trajectory of the robot is planned to optimize the SLAM results. A planning strategy combining an efficient global planner with an accurate local planner is proposed to solve the problem. Simulation results under different scenarios have shown that the proposed active SLAM algorithm provides a good balance between accuracy and efficiency as compared to the local planner and the global planner.

2021

1. ICRA

   

   Invariant EKF based 2D Active SLAM with Exploration Task

   Mengya Xu, Yang Song, Yongbo Chen, Shoudong Huang, and Qi Hao

   In IEEE International Conference on Robotics and Automation (ICRA), 2021

   Abs DOI Code

   Right invariant extended Kalman filter (RIEKF) based simultaneous localization and mapping (SLAM) proposed recently has shown to be able to produce more consistent SLAM estimates as compared with traditional EKF based SLAM methods, including some improved EKF SLAM methods such as observability constrained-EKF (OC-EKF) SLAM. Latest results have demonstrated that its performance is very close to optimization based SLAM algorithms such as iSAM. In this paper, we propose to use RIEKF SLAM algorithm in active SLAM where both the predicted SLAM results for choosing control actions and the actual estimated SLAM results applying the selected control actions are computed using RIEKF algorithms. The advantages over traditional EKF based active SLAM are the more accurate and consistent predicted uncertainty estimates which result in robustness of the active SLAM algorithm. The advantages over optimization based active SLAM is the reduced computational cost. Simulation results are presented to validate the advantages of the proposed algorithm

2. IROS

   

   Some Research Questions for SLAM in Deformable Environments

   Shoudong Huang, Yongbo Chen, Liang Zhao, Yanhao Zhang, and Mengya Xu

   In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

   Abs DOI Code

   This paper considers some research questions for SLAM in deformable environments. The first question is how to use the SLAM results to plan the trajectory of the robot. The second question is how to use the SLAM results to plan the trajectory of the robot. The third question is how to use the SLAM results to plan the trajectory of the robot. The fourth question is how to use the SLAM results to plan the trajectory of the robot. The fifth question is how to use the SLAM results to plan the trajectory of the robot. The sixth question is how to use the SLAM results to plan the trajectory of the robot. The seventh question is how to use the SLAM results to plan the trajectory of the robot. The eighth question is how to use the SLAM results to plan the trajectory of the robot. The ninth question is how to use the SLAM results to plan the trajectory of the robot. The tenth question is how to use the SLAM results to plan the trajectory of the robot.