Biologically Inspired Mobile Robot Vision Localization

We present a robot localization system using biologically inspired vision. Our system models two extensively studied human visual capabilities: 1) extracting the gist of a scene to produce a coarse localization hypothesis and 2) refining it by locating salient landmark points in the scene. Gist is computed here as a holistic statistical signature of the image, thereby yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, which efficiently directs the time-consuming landmark-identification process toward the most likely candidate locations in the image. The gist features and salient regions are then further processed using a Monte Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments-building complex (38.4m x 54.86 m area, 13 966 testing images), vegetation-filled park (82.3m x 109.73 m area, 26 397 testing images), and open-field park (137.16 m x 178.31 m area, 34 711 testing images)-each with its own challenges. The system is able to localize, on average, within 0.98, 2.63, and 3.46 m, respectively, even with multiple kidnapped-robot instances.