Efficient WiFi LiDAR SLAM for Autonomous Robots in Large Environments

Autonomous robots operating in indoor and GPS denied environments can use LiDAR for SLAM instead. However, LiDARs do not perform well in geometrically-degraded environments, due to the challenge of loop closure detection and computational load to perform scan matching. Existing WiFi infrastructure c...

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Bibliographic Details
Published inarXiv.org
Main Authors Ismail, Khairuldanial, Liu, Ran, Qin, Zhenghong, Athukorala, Achala, Billy Pik Lik Lau, Shalihan, Muhammad, Chau Yuen, U-Xuan Tan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.06.2022
Subjects
Computational efficiency
Computing costs
Fingerprints
Geometric accuracy
Indoor environments
Lidar
Load matching
Pose estimation
Robots
Root-mean-square errors
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Summary:Autonomous robots operating in indoor and GPS denied environments can use LiDAR for SLAM instead. However, LiDARs do not perform well in geometrically-degraded environments, due to the challenge of loop closure detection and computational load to perform scan matching. Existing WiFi infrastructure can be exploited for localization and mapping with low hardware and computational cost. Yet, accurate pose estimation using WiFi is challenging as different signal values can be measured at the same location due to the unpredictability of signal propagation. Therefore, we introduce the use of WiFi fingerprint sequence for pose estimation (i.e. loop closure) in SLAM. This approach exploits the spatial coherence of location fingerprints obtained while a mobile robot is moving. This has better capability of correcting odometry drift. The method also incorporates LiDAR scans and thus, improving computational efficiency for large and geometrically-degraded environments while maintaining the accuracy of LiDAR SLAM. We conducted experiments in an indoor environment to illustrate the effectiveness of the method. The results are evaluated based on Root Mean Square Error (RMSE) and it has achieved an accuracy of 0.88m for the test environment.
ISSN:2331-8422