Adaptive Trajectory Estimation with Power Limited Steering Model under Perturbation Compensation
share
Trajectory estimation under regional correlations is applied in numerous tasks like dynamic path planning, navigation and tracking. Many previous works get impressive results in the strictly controlled condition with accurate prior statistics and specific dynamic model for certain object. While in a more challenging situation without specific dynamic model and accurate prior statistics, the performance of these methods significantly declines. To estimate the trajectory without specific dynamic model, we propose a general model called the power-limited steering model (PLS), which is a natural combination of instantaneous power and instantaneous angular velocity. As a nonlinear model it needs less parameter to describe the switching of states compared with linear Markov process model. Then we derive the corresponding form in discrete time and compensate the nonlinear effect of perturbation with renormalization group. To overcome the biased prior statistics, the observations drift and linear growing computation in trajectory estimation, we propose the adaptive trajectory estimation (AdaTE) where the online updated statistics and the adaptive truncation time is applied. The experiment of typical trajectory estimation demonstrates that compared with EKF and UKF, AdaTE is more robust to the biased prior statistics and observation drift. Another task shows that with slight modification, AdaTE can be used in path planning that needs obstacle avoidance. Finally, we exhibit its potential application on trajectory optimization in visual tracking.
READ FULL TEXT