点群を用いた階層型深層オドメトリ推定
Point Cloud based Hierarchical Deep Odometry Estimation
走行シーンの点群データからフレーム間オドメトリを推定する階層型深層モデルを提案し、LSTMを用いた局所バンドル調整版も実装して最先端手法と比較評価した。
著者: Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Robert Laganiere
分類: cs.CV, cs.CG, cs.RO
原文アブストラクト
Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using point clouds. In this paper, we propose a deep model that learns to estimate odometry in driving scenarios using point cloud data. The proposed model consumes raw point clouds in order to extract frame-to-frame odometry estimation through a hierarchical model architecture. Also, a local bundle adjustment variation of this model using LSTM layers is implemented. These two approaches are comprehensively evaluated and are compared against the state-of-the-art.
