Object-based localization and mapping using loop constraints and geometric prior knowledge

摘要

This paper presents a method of building a structured map, which consists of objects such as furniture. We represent a map as a graph, in which a node represents an object and an arc represents a relative pose between objects. The robot localizes itself and builds a map using odometry readings and sensor data obtained by object recognition. To correct the map distortion caused by errors in the data, the robot utilizes loops as geometric constraints, and imports geometric knowledge provided by a hand-made map. Experiments show that the root successfully built a map of corridors, and a map of a room having many objects.