Invariant surface and motion estimation from sparse range data

In this paper, we present a system for the estimation of the surface structure and the motion parameters of a free-flying object in a tele-robotics experiment. The system consists of two main components: (i) a vision-based invariant-surface and motion estimator and (ii) a Kalman filter state estimator. We present a new algorithm for motion estimation from sparse multi-sensor range data. The motion estimates from the vision-based estimator are input to a Kalman filter state estimator for continuously tracking a free-flying object in space under zero-gravity conditions. The predicted position and orientation parameters are then fed back to the vision module of the system and serve as an initial guess in the search for optimal motion parameters. The task of the vision module is two-fold: (i) estimating a piecewise-smooth surface from a single frame of multi-sensor data and (ii) determining the most likely (in the Bayesian sense) object motion that makes data in subsequent time frames to have been sampled from the same piecewise-smooth surface. With each incoming data frame, the piecewise-smooth surface is incrementally refined. The problem is formulated as an energy minimization and solved numerically resulting in a surface estimate invariant to 3D rigid motion and the vector of motion parameters. Performance of the system is depicted on simulated and real range data.