意味論と不確実性を活用した視覚SLAM向け特徴選択
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
ベイズニューラルネットの分類不確実性と意味セグメンテーションを情報理論的に組み込み、長期局所化に有効な特徴点を選ぶ視覚SLAM手法SIVOを提案。地図サイズを約70%削減しつつ従来手法と同等の性能を示した。
著者: Pranav Ganti, Steven L. Waslander
分類: cs.CV, cs.RO
原文アブストラクト
In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection can help ensure that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates semantic segmentation and neural network uncertainty into the feature selection pipeline. Our algorithm selects points which provide the highest reduction in Shannon entropy between the entropy of the current state and the joint entropy of the state, given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Each selected feature significantly reduces the uncertainty of the vehicle state and has been detected to be a static object (building, traffic sign, etc.) repeatedly with a high confidence. This selection strategy generates a sparse map which can facilitate long-term localization. The KITTI odometry dataset is used to evaluate our method, and we also compare our results against ORB_SLAM2. Overall, SIVO performs comparably to the baseline method while reducing the map size by almost 70%.
