氷水境界探査ロボットのための密結合視覚-DVL-慣性オドメトリ
Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration
視覚・DVL・IMU・圧力センサを密結合したMSCKFベースの状態推定手法を提案し、氷下での実地データで従来の6手法より高精度な自己位置推定を実現した。
著者: Lin Zhao, Mingxi Zhou, Brice Loose
分類: cs.RO, cs.CV
原文アブストラクト
Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for collecting biogeochemical data at the ice-water interface for scientific advancements. However, state estimation, i.e., localization, is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper, we present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated into the state-of-art Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides that a new keyframe-based state clone mechanism and a new DVL-aided feature enhancement are presented to further improve the localization performance. The proposed method is validated with a data set collected in the field under frozen ice, and the result is compared with 6 other different sensor fusion setups. Overall, the result with the keyframe enabled and DVL-aided feature enhancement yields the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path with a total traveling distance of about 200 m.
