TOPGN: Real-time Transparent Obstacle Detection using Lidar Point Cloud Intensity for Autonomous Robot Navigation

TL;DR

TOPGN is a real-time lidar-based method that detects transparent obstacles for autonomous robots, improving safety and navigation accuracy in complex environments by leveraging intensity data and multi-layer grid mapping.

Contribution

The paper introduces a novel real-time transparent obstacle detection technique using lidar intensity data and multi-layer grid maps, outperforming existing RGB and laser-based methods.

Findings

Achieves at least 12.74% higher F-score in transparent object detection.

Reduces mean absolute error by up to 38.46%.

Doubles navigation success rates compared to previous methods.

Abstract

We present TOPGN, a novel method for real-time transparent obstacle detection for robot navigation in unknown environments. We use a multi-layer 2D grid map representation obtained by summing the intensities of lidar point clouds that lie in multiple non-overlapping height intervals. We isolate a neighborhood of points reflected from transparent obstacles by comparing the intensities in the different 2D grid map layers. Using the neighborhood, we linearly extrapolate the transparent obstacle by computing a tangential line segment and use it to perform safe, real-time collision avoidance. Finally, we also demonstrate our transparent object isolation's applicability to mapping an environment. We demonstrate that our approach detects transparent objects made of various materials (glass, acrylic, PVC), arbitrary shapes, colors, and textures in a variety of real-world indoor and outdoor scenarios with varying lighting conditions. We compare our method with other glass/transparent object detection methods that use RGB images, 2D laser scans, etc. in these benchmark scenarios. We demonstrate superior detection accuracy in terms of F-score improvement at least by 12.74% and 38.46% decrease in mean absolute error (MAE), improved navigation success rates (at least two times better than the second-best), and a real-time inference rate (~50Hz on a mobile CPU). We will release our code and challenging benchmarks for future evaluations upon publication.