<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor fusion SLAM algorithm for indoor robots was proposed. Aiming at the mismatch problem of the traditional ICP algorithm in the front end of the lidar SLAM, the algorithm adopts the PL-ICP algorithm that is more suitable for the indoor environment, and uses the extended Kalman filter to fuse the wheel odometer and IMU to provide the initial motion estimation value. Then, during the mapping phase, the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to compensate for the lack of vertical field of view in the 2D lidar mapping. The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33% compared to a single wheeled odometer, providing a higher initial iteration value for the PL-ICP algorithm. At the same time, fusion mapping compensates for the shortcomings of a single two-dimensional lidar mapping, and constructs an environmental map with more complete environmental information.</p>
</abstract>

In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor fusion SLAM algorithm for indoor robots was proposed. Aiming at the mismatch problem of the traditional ICP algorithm in the front end of the lidar SLAM, the algorithm adopts the PL-ICP algorithm that is more suitable for the indoor environment, and uses the extended Kalman filter to fuse the wheel odometer and IMU to provide the initial motion estimation value. Then, during the mapping phase, the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to compensate for the lack of vertical field of view in the 2D lidar mapping. The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33% compared to a single wheeled odometer, providing a higher initial iteration value for the PL-ICP algorithm. At the same time, fusion mapping compensates for the shortcomings of a single two-dimensional lidar mapping, and constructs an environmental map with more complete environmental information.

Indoor Robot SLAM with Multi-Sensor Fusion

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Indoor Robot SLAM with Multi-Sensor Fusion"}

In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor...

Indoor Robot SLAM with Multi-Sensor Fusion

Publié en ligne: 28 mars 2024

Pages: 10 - 21

DOI: https://doi.org/10.2478/ijanmc-2024-0002

Mots clés
SLAM, Indoor Robot, Extended Kalman Filter, PL-ICP

© 2024 Jionglin He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Indoor Robot SLAM with Multi-Sensor Fusion

Publié en ligne: 28 mars 2024

Pages: 10 - 21

DOI: https://doi.org/10.2478/ijanmc-2024-0002

Mots clésSLAM, Indoor Robot, Extended Kalman Filter, PL-ICP

© 2024 Jionglin He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
SLAM, Indoor Robot, Extended Kalman Filter, PL-ICP