Single Vision-Based Self-Localization for Autonomous Robotic Agents

We present a single vision-based, self-localization method for autonomous mobile robots in a known, indoor environment. This absolute localization method is landmark assisted, therefore, we propose an algorithm that requires the extraction of a single landmark feature i.e., the length of a known edge. Our technique is based on measuring the distance from two distinct, arbitrarily positioned landmarks in the robot's environment, the locations of which are known a priori. A single camera vision system is used to perform distance estimation. The developed framework is applied to tracking a robot's pose, i.e., its position and orientation, in a Cartesian coordinate system. The position of the robot is estimated using a bilateration method, while its orientation calculation utilizes tools from projective geometry. The validity and feasibility of the approach are demonstrated through experiments.