Lidar Positioning for Indoor Precision Navigation

Abstract

Lidar based simultaneous localization and mapping methods can be adapted for deployment on small autonomous vehicles operating in unmapped indoor environments. For this purpose, we propose a method which combines inertial data, low-drift lidar odometry, planar primitives, and loop closing in a graph-based structure. The accuracy of our method is experimentally evaluated, using a high-resolution lidar, and compared to the state-ofthe-art methods LIO-SAM and Cartographer. We specifically address the lateral positioning accuracy when passing through narrow openings, where high accuracy is a prerequisite for safe operation of autonomous vehicles. The test cases include doorways, slightly wider reference passages, and a larger corridor environment. We observe a reduced lateral accuracy for all three methods when passing through the narrow openings compared to operation in larger spaces. Compared to state-of-the-art, our method shows better results in the narrow passages, and comparable results in the other environments with reasonably low usage of CPU and memory resources.

A Novel Model-Free Adaptive Proportional–Integral–Derivative Control Method for Speed-Tracking Systems of Electric Balanced Forklifts

“…Due to the reasons outlined above, research on automatic driving technology for forklifts has become a hot topic, especially in the areas of visual positioning [8], lidar positioning [9], and adaptive control [10]. For instance, the Vecna company (Cambridge, MA, USA) in the US has launched an automatic navigation forklift that employs multiple sensing technologies, such as lidar, an inertial navigation system, and visual sensors, to achieve accurate positioning and path planning [11].…”