Biologically-inspired robotics vision monte-carlo localization in the outdoor environment

Abstract

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 regions in the scene. Gist is computed here as a holistic statistical signature of the image, yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, efficiently directing the time-consuming landmark identification process towards the most likely candidate locations in the image. The gist and salient landmark features 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 (126times180 ft. area, 3794 testing images), vegetation-filled park (270times360 ft. area, 7196 testing images), and open-field park (450times585 ft. area, 8287 testing images) - each with its own challenges. The system is able to localize, on average, within 6.0, 10.73, and 32.24 ft., respectively, even with multiple kidnapped-robot instances.