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arXiv · 2306.15226

Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models

Eric Chen·Cherie Ho·Mukhtar Maulimov·Chen Wang·Sebastian Scherer
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Abstract

Autonomous offroad driving is essential for applications like emergency rescue, military operations, and agriculture. Despite progress, systems struggle with high-speed vehicles exceeding 10m/s due to the need for accurate long-range (> 50m) perception for safe navigation. Current approaches are limited by sensor constraints; LiDAR-based methods offer precise short-range data but are noisy beyond 30m, while visual models provide dense long-range measurements but falter with unseen scenarios. To overcome these issues, we introduce ALTER, a learning-on-the-drive perception framework that leverages both sensor types. ALTER uses a self-supervised visual model to learn and adapt from near-range LiDAR measurements, improving long-range prediction in new environments without manual labeling. It also includes a model selection module for better sensor failure response and adaptability to known environments. Testing in two real-world settings showed on average 43.4% better traversability prediction than LiDAR-only and 164% over non-adaptive state-of-the-art (SOTA) visual semantic methods after 45 seconds of online learning.

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Eric Chen, Cherie Ho, Mukhtar Maulimov, Chen Wang, Sebastian Scherer. 2024-10-13. Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models. https://arxiv.org/abs/2306.15226

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