“Off the grid”: Self-contained landmarks for improved indoor probabilistic localization

Abstract

Indoor localization is a challenging problem, especially in dynamically changing environments and in the presence of sensor errors such as odometry drift. We present a method for robustly localizing a robot in realistic indoor environments. We improve a popular probabilistic approach called Monte Carlo localization, which estimates the robot's position using depth features of the environment and is prone to errors when the topology changes (e.g., due to a moved piece of furniture). We propose a technique that improves localization by augmenting the environment with a set of QR code landmarks. Each landmark embeds information about its 3D pose relative to the world coordinate system, the same coordinate system as the map. Our algorithm detects the landmarks in images from an RGB-D camera, uses depth information to estimates their pose relative to the robot, and incorporates the resulting position evidence in a probabilistic manner. We conducted experiments on an iRobot ATRV-JR robot and show that our method is more reliable in dynamic environments than the exclusively probabilistic localization method.