CV-SLAM: a new ceiling vision-based SLAM technique

Abstract

We propose a fast and robust CV-SLAM (ceiling vision-based simultaneous localization and mapping) technique using a single ceiling vision sensor. The proposed algorithm is suitable for system that demands very high localization accuracy such as an intelligent robot vacuum cleaner. A single camera looking upward direction (called ceiling vision system) is mounted on the robot, and salient image features are detected and tracked through the image sequence. Compared with the conventional frontal view systems, the ceiling vision has advantage in tracking, since it involves only rotation and affine transform without scale change. And, in this paper, we solve the rotation and affine transform problems using 3D gradient orientation estimation method and multi-view description of landmarks. By applying these methods to the solution for data association, we can reconstruct the 3D landmark map in real-time through the extend Kalman filter based SLAM framework. Furthermore, relocation problem is solved efficiently by using a wide base line matching between the reconstructed 3D map and a 2D ceiling image. Experimental results demonstrate the accuracy and robustness of the proposed algorithm in real environments.