A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles

Simultaneous localization and mapping (SLAM) is a fundamental technique block in the indoor-navigation system for most autonomous vehicles and robots. SLAM aims at building a global consistent map of the environment while simultaneously determining the position and orientation of the robot in this map. Significant advances have been made in visual SLAM techniques in the past several years. However, due to the fragile performance in tracking feature points in environments that lack texture, e.g., a warehouse with blank white walls, visual SLAM can hardly provide a reliable localization. Compared with visual SLAM, LiDAR SLAM can often provide more robust localization in indoor environments by using 3D spatial information directly captured by LiDAR point clouds. Thus, LiDAR SLAM techniques are often employed in industrial applications such as automated guided vehicles (AGVs). In the past decades, a number of LiDAR SLAM methods have been proposed. However, the strength and weakness points of various LiDAR SLAMs are not clear, which may perplex the researchers and engineers. In this article, analysis and comparisons are made on different LiDAR SLAM-based indoor navigation methods, and extensive experiments are conducted to evaluate their performances in real environments. The comparative analysis and results can help researchers in academia and industry in constructing a suitable LiDAR SLAM system for indoor navigation for their own usage scenarios.

Automated summary

SLAM is crucial for indoor navigation in autonomous vehicles and robots, with visual SLAM having drawbacks in tracking feature points in environments lacking texture. On the other hand, LiDAR SLAM can offer more robust localization by utilizing 3D spatial information directly captured by LiDAR point clouds.