Efficient WiFi LiDAR SLAM for Autonomous Robots in Large Environments

TL;DR

This paper presents a novel SLAM method combining WiFi fingerprint sequences with LiDAR data to improve localization accuracy and computational efficiency for autonomous robots in large, geometrically-degraded indoor environments.

Contribution

It introduces WiFi fingerprint sequence-based loop closure detection integrated with LiDAR SLAM, enhancing accuracy and efficiency in challenging environments.

Findings

Achieved 0.88m RMSE accuracy in indoor tests.

Improved loop closure detection using WiFi fingerprints.

Enhanced computational efficiency for large-scale environments.

Abstract

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.