走行しながら学習：視覚ベースのオフロード走行可能性モデルの自己教師あり適応
Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models
近距離LiDARを教師信号に使い、視覚モデルを走行中に自己教師ありで適応させて長距離の走行可能領域予測を改善するフレームワークALTERを提案。実環境でLiDAR単体より43.4%、非適応のSOTA視覚手法より164%精度が向上。
著者: Eric Chen, Cherie Ho, Mukhtar Maulimov, Chen Wang, Sebastian Scherer
分類: cs.RO
原文アブストラクト
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.
