Visual-inertial simultaneous localization, mapping and sensor-to-sensor self-calibration
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A robust null space method for linear equality constrained state estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration
International Journal of Robotics Research
Truncation nonlinear filters for state estimation with nonlinear inequality constraints
Automatica (Journal of IFAC)
Dense map inference with user-defined priors: from priorlets to scan eigenvariations
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
Compressive system identification: Sequential methods and entropy bounds
Digital Signal Processing
| Hi-index | 35.69 |
The state space description of some physical systems possess nonlinear equality constraints between some state variables. In this paper, we consider the problem of applying a Kalman filter-type estimator in the presence of such constraints. We categorize previous approaches into pseudo-observation and projection methods and identify two types of constraints-those that act on the entire distribution and those that act on the mean of the distribution. We argue that the pseudo-observation approach enforces neither type of constraint and that the projection method enforces the first type of constraint only. We propose a new method that utilizes the projection method twice-once to constrain the entire distribution and once to constrain the statistics of the distribution. We illustrate these algorithms in a tracking system that uses unit quaternions to encode orientation