Invariant EKF Design for Scan Matching-aided Localization
This addresses indoor robot localization, but it is incremental as it applies known filter variants to a specific setup.
The paper tackled robot localization in indoor environments by developing Invariant and Multiplicative Extended Kalman Filters that fuse motion sensor data with scan matching from a Kinect camera, showing the IEKF design's advantage in experiments.
Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.