Online Adaptive Traversability Estimation through Interaction for Unstructured, Densely Vegetated Environments

2025-02-04

AI Summary

To address degraded traversability estimation performance on out-of-distribution (OOD) terrains—such as dense vegetation—in unstructured environments, this paper proposes a LiDAR-only perception-based online adaptation method. The approach leverages robot–environment interaction to collect self-supervised data for real-time updates of a probabilistic 3D voxel model. It innovatively integrates a sparse graph neural network with probabilistic voxel representation and introduces a lightweight online self-supervised learning mechanism, enabling efficient deployment on edge hardware (e.g., a 25W GPU). With only eight minutes of real-world vehicle data, the method achieves an overall Matthews Correlation Coefficient (MCC) of 0.63, enabling safe navigation across natural, unstructured terrain. Its accuracy matches that of full offline models while significantly improving generalization to unknown scenes and real-time adaptability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Navigating densely vegetated environments poses significant challenges for autonomous ground vehicles. Learning-based systems typically use prior and in-situ data to predict terrain traversability but often degrade in performance when encountering out-of-distribution elements caused by rapid environmental changes or novel conditions. This paper presents a novel, lidar-only, online adaptive traversability estimation (TE) method that trains a model directly on the robot using self-supervised data collected through robot-environment interaction. The proposed approach utilises a probabilistic 3D voxel representation to integrate lidar measurements and robot experience, creating a salient environmental model. To ensure computational efficiency, a sparse graph-based representation is employed to update temporarily evolving voxel distributions. Extensive experiments with an unmanned ground vehicle in natural terrain demonstrate that the system adapts to complex environments with as little as 8 minutes of operational data, achieving a Matthews Correlation Coefficient (MCC) score of 0.63 and enabling safe navigation in densely vegetated environments. This work examines different training strategies for voxel-based TE methods and offers recommendations for training strategies to improve adaptability. The proposed method is validated on a robotic platform with limited computational resources (25W GPU), achieving accuracy comparable to offline-trained models while maintaining reliable performance across varied environments.

Problem

• Adaptive traversability estimation in dense vegetation
• Online learning from robot-environment interaction
• Efficient computation for real-time navigation

Innovation

• Lidar-only online adaptive estimation
• Probabilistic 3D voxel representation
• Sparse graph-based computational efficiency