Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation

TL;DR

This paper introduces Circular Accessible Depth (CAD), a novel, robust traversability representation for UGV navigation that leverages a neural network with attention mechanisms to predict traversable borders from LiDAR data, improving robustness and accuracy.

Contribution

The paper proposes CAD, a new polar-coordinate based traversability measure, and CADNet, a neural network with attention modules, enabling semi-supervised learning and better real-world performance.

Findings

CAD outperforms baselines in robustness and precision

CADNet effectively encodes spatial features from LiDAR data

Real-world UGV experiments validate the approach

Abstract

In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module, Stability-Attention Module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semi-supervised learning for the CADNet, and thus desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.