火星走行性予測：マルチモーダル自己教師あり学習によるコストマップ生成
Mars Traversability Prediction: A Multi-modal Self-supervised Approach for Costmap Generation
カメラとLiDARを融合し、IMU由来のラベルで自己教師あり学習する惑星探査ローバー向けの走行性コストマップ予測フレームワークを提案。
詳しい要約
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著者: Zongwu Xie, Kaijie Yun, Yang Liu, Yiming Ji, Han Li
分類: cs.CV, cs.RO
原文アブストラクト
We present a robust multi-modal framework for predicting traversability costmaps for planetary rovers. Our model fuses camera and LiDAR data to produce a bird's-eye-view (BEV) terrain costmap, trained self-supervised using IMU-derived labels. Key updates include a DINOv3-based image encoder, FiLM-based sensor fusion, and an optimization loss combining Huber and smoothness terms. Experimental ablations (removing image color, occluding inputs, adding noise) show only minor changes in MAE/MSE (e.g. MAE increases from ~0.0775 to 0.0915 when LiDAR is sparsified), indicating that geometry dominates the learned cost and the model is highly robust. We attribute the small performance differences to the IMU labeling primarily reflecting terrain geometry rather than semantics and to limited data diversity. Unlike prior work claiming large gains, we emphasize our contributions: (1) a high-fidelity, reproducible simulation environment; (2) a self-supervised IMU-based labeling pipeline; and (3) a strong multi-modal BEV costmap prediction model. We discuss limitations and future work such as domain generalization and dataset expansion.
関連論文
- 不整地走行性を多目的に学習する手法走行性予測
