プロキシバンク誘導による自己教師あり3D走行可能性推定
Self-Supervised 3D Traversability Estimation with Proxy Bank Guidance
オフロード環境での走行可能性推定において、少数の正負プロトタイプと未ラベルデータを深層距離学習で活用し、セマンティックセグメンテーションと回帰を統合的に学習する手法を提案。新評価指標とオフロードデータセットDtrailも構築した。
著者: Jihwan Bae, Junwon Seo, Taekyung Kim, Hae-gon Jeon, Kiho Kwak, Inwook Shim
分類: cs.RO, cs.CV
原文アブストラクト
Traversability estimation for mobile robots in off-road environments requires more than conventional semantic segmentation used in constrained environments like on-road conditions. Recently, approaches to learning a traversability estimation from past driving experiences in a self-supervised manner are arising as they can significantly reduce human labeling costs and labeling errors. However, the self-supervised data only provide supervision for the actually traversed regions, inducing epistemic uncertainty according to the scarcity of negative information. Negative data are rarely harvested as the system can be severely damaged while logging the data. To mitigate the uncertainty, we introduce a deep metric learning-based method to incorporate unlabeled data with a few positive and negative prototypes in order to leverage the uncertainty, which jointly learns using semantic segmentation and traversability regression. To firmly evaluate the proposed framework, we introduce a new evaluation metric that comprehensively evaluates the segmentation and regression. Additionally, we construct a driving dataset `Dtrail' in off-road environments with a mobile robot platform, which is composed of a wide variety of negative data. We examine our method on Dtrail as well as the publicly available SemanticKITTI dataset.
