Detection and Utilization of Reflections in LiDAR Scans Through Plane Optimization and Plane SLAM

TL;DR

This paper introduces a method for detecting and utilizing reflective planes in LiDAR scans through plane optimization and SLAM, improving classification accuracy of reflections for better localization and mapping.

Contribution

The work advances LiDAR-based mapping by constructing a global optimized map of reflective planes, enabling more accurate classification of reflective points over single-scan methods.

Findings

Superior classification accuracy compared to single-scan approaches

Effective detection and mapping of reflective planes in LiDAR data

Open source code and data available for replication

Abstract

In LiDAR sensing, glass, mirrors and other material often cause inconsistent data readings, because the laser beams may report the distance of the glass, the distance of the object behind the glass or the distance to a reflected object. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and thus navigation. With dual-return LiDARs and other methods, one can detect the glass plane and classify the points in a single scan. In this work we go one step further and construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.