Cost-Effective Mapping of Mobile Robot Based on the Fusion of UWB and Short-Range 2-D LiDAR

Environment mapping is an essential prerequisite for mobile robots to perform different tasks such as navigation and mission planning. With the availability of low-cost 2-D LiDARs, there are increasing applications of such 2-D LiDARs in industrial environments. However, environment mapping in an unknown and featureless environment with such low-cost 2-D LiDARs remains a challenge. The challenge mainly originates from the short range of LiDARs and complexities in performing scan matching in these environments. In order to resolve these shortcomings, we propose to fuse the ultrawideband (UWB) with 2-D LiDARs to improve the mapping quality of a mobile robot. The optimization-based approach is utilized for the fusion of UWB ranging information and odometry to first optimize the trajectory. Then, the LiDAR-based loop closures are incorporated to improve the accuracy of the trajectory estimation. Finally, the optimized trajectory is combined with the LiDAR scans to produce the occupancy map of the environment. The performance of the proposed approach is evaluated in an indoor featureless environment with a size of 20 x 20 m. Obtained results show that the mapping error of the proposed scheme is 85.5% less than that of the conventional GMapping algorithm with short-range LiDAR (for example, Hokuyo URG-04LX in our experiment with a maximum range of 5.6 m).

Automated summary

This article discusses the challenges and solution for environment mapping in unknown and featureless environments using low-cost 2D LiDARs. The proposed approach combines ultrawideband (UWB) with 2D LiDARs to improve mapping quality of mobile robots by optimizing trajectory and incorporating LiDAR-based loop closures. The results show a significant reduction in mapping error compared to the conventional GMapping algorithm with short-range LiDAR.