Mobile Robot Motion Tracking Using Descriptor Matching and Sensor Fusion

This paper presents fast tracking of a mobile robots 2D pose in a plane using the open source computer vision library(OpenCV). This can be useful for setting up experiments to study mobile robot control, robot formation or conflict resolution. Here the feature detectors SIFT, AKAZE and ORB are tested for their speed and accuracy for tracking a robot on a plane of size 2.7m × 2.1m. To determine the accuracy that can be achieved they are compared against an edge-based template matching algorithm which has a known accuracy. First the accuracy vs detection time is studied on different size images. Then sensor fusion is studied by combining the extended Kalman filter (EKF) and unscented Kalman filter (UKF) with odometry to see what gains can be made. Root mean squared pose errors of less than 3mm in translation and less than 1 degree in heading are achieved at a object detection times of less than 50ms.