Collision-Aware Traversability Analysis for Autonomous Vehicles in the Context of Agricultural Robotics

TL;DR

This paper presents a novel 3D spectral map-based traversability analysis method for agricultural robots, enabling safe navigation by distinguishing deformable obstacles from rigid ones using multispectral data.

Contribution

It introduces a new approach combining multispectral imaging and physics-based metrics for improved obstacle detection and traversability analysis in unstructured agricultural environments.

Findings

Effective differentiation between deformable and rigid obstacles.

Enhanced safety in autonomous agricultural navigation.

Integration of multispectral data into environmental mapping.

Abstract

In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles.