Year: 2012
Type: conference-paper
Abstract: We present a camera-based 3D feature tracking method, integrated into a bank of iterated, extended Kalman filters (IEKF), associated to each visual feature. The approach exploits motion field to estimate velocities of the rigid body. The depths are required only for initialization, and can be obtained either from joint estimation of depth and motion or from stereo correspondence. The motion of each 3D point cloud is predicted under a common rigid velocity constraint. A robust pose estimator, based on dual-quaternions and median statistics, is further applied to the estimated points. In case of temporarily missing measurements, the last estimated body velocity is used to predict the next poses. Results are shown on images of a satellite-mockup, to demonstrate performances for on-orbit servicing in space environment.
Source: elib (German Aerospace Center)
Authors: Nassir W. Oumer, Giorgio Panin
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2223713969