物体残差拘束付き視覚慣性オドメトリ
OrcVIO: Object residual constrained Visual-Inertial Odometry
物体の意味情報とバウンディングボックス再投影誤差を活用し、物体の姿勢・形状とIMUカメラ状態を同時最適化する視覚慣性オドメトリ手法を提案。
著者: Mo Shan, Vikas Dhiman, Qiaojun Feng, Jinzhao Li, Nikolay Atanasov
分類: cs.RO, cs.CV
原文アブストラクト
Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical. It not only improves the performance but also enables tasks specified in terms of meaningful objects. This work presents OrcVIO, for visual-inertial odometry tightly coupled with tracking and optimization over structured object models. OrcVIO differentiates through semantic feature and bounding-box reprojection errors to perform batch optimization over the pose and shape of objects. The estimated object states aid in real-time incremental optimization over the IMU-camera states. The ability of OrcVIO for accurate trajectory estimation and large-scale object-level mapping is evaluated using real data.
