Object Goal Navigation Based on Semantics and RGB Ego View
This addresses the challenge of object goal navigation for service robots in unknown settings, though it appears incremental as it builds on existing semantic and geometric mapping techniques.
The paper tackles the problem of enabling a service robot to navigate unknown indoor environments using only RGB ego-view to find a target object, and it outperforms human users in average completion time in evaluations.
This paper presents an architecture and methodology to empower a service robot to navigate an indoor environment with semantic decision making, given RGB ego view. This method leverages the knowledge of robot's actuation capability and that of scenes, objects and their relations -- represented in a semantic form. The robot navigates based on GeoSem map - a relational combination of geometric and semantic map. The goal given to the robot is to find an object in a unknown environment with no navigational map and only egocentric RGB camera perception. The approach is tested both on a simulation environment and real life indoor settings. The presented approach was found to outperform human users in gamified evaluations with respect to average completion time.