This paper presents a new method to optimally combine motion measurements provided by proprioceptive sensors, with relative-state estimates inferred from feature-based matching. Two key challenges arise in such pose tracking problems: 1) the displacement estimates relate the state of the robot at two different time instants and 2) the same exteroceptive measurements are often used for computing consecutive displacement estimates, a process that renders the errors in these correlated. We present a novel stochastic cloning Kalman filtering (SC-KF) estimation algorithm that successfully addresses these challenges, while still allowing for efficient calculation of the filter gains and covariances. The proposed algorithm is not intended to compete with simultaneous localization and mapping (SLAM) approaches. Instead, it can be merged with any extended-Kalman-filter-based SLAM algorithm to increase its precision. In this respect, the SC-KF provides a robust framework for leveraging additional motion information extracted from dense point features that most SLAM algorithms do not treat as landmarks. Extensive experimental and simulation results are presented to verify the validity of the proposed method and to demonstrate that its performance is superior to that of alternative position-tracking approaches.
Bibliographical noteFunding Information:
Manuscript received March 1, 2006; revised December 18, 2006. This paper was recommended for publication by Associate Editor W. Burgard and Editor L. Parker upon evaluation of the reviewers’ comments. This work was supported in part by the University of Minnesota (DTC), in part by the NASA Mars Technology Program (MTP-1263201), and in part by the National Science Foundation (EIA-0324864, IIS-0643680).
- Displacement estimates
- Relative-pose measurements
- Robot localization
- State augmentation
- Stochastic cloning