Edge detection based mobile robot indoor localization

Abstract

In this paper, we present the precise indoor positioning system for mobile robot pose estimation based on visual edge detection. The set of onboard motion sensors (i.e. wheel speed sensor and yaw rate sensor) is used for pose prediction. A schematic plan of the building, stored as a multichannel raster image, is used as a prior information. The pose likelihood estimation is performed via matching of edges, detected on the optical image, against the map. Therefore, the proposed method does not require any deliberate building infrastructure changes and makes use of the inherent features of manmade structures - edges between walls and floor. The particle filter algorithm is applied in order to integrate heterogeneous localization data (i.e. motion sensors and detected visual features). Since particle filter uses probabilistic sensor models for state estimation, the precise measurement noise modeling is key to positioning quality enhancement. The probabilistic noise model of the edge detector, combining geometrical detection noise and false positive edge detection noise, is proposed in this work. Developed localization system was experimentally evaluated on the car-like mobile robot in the challenging environment. Experimental results demonstrate that the proposed localization system is able to estimate the robot pose with a mean error not exceeding 0.1 m on each of 100 test runs.