Accurate On-line Extrinsic Calibration for a Multi-camera SLAM System

Abstract

Simultaneous localization and mapping (SLAM) system has an important role in providing an accurate and comprehensive solution for situational awareness in unknown environments. In order to maximize the situational awareness, the wider field of view is required. It is possible to achieve a wide field of view with an omnidirectional lense or multiple perspective cameras. However, calibration of such systems is sensitive and difficult. For this reason, we present a practical solution to a multi-camera SLAM system. The goal of this study is to obtain robust localization and mapping for multi-camera setup without requiring pre-calibration of the camera system calibration. With this goal, we associate measurements from cameras with their relative poses and propose an iterative optimization method to refine the map, keyframe poses and relative poses between cameras simultaneously. We evaluated our method on a dataset which consists of three cameras with small overlapping regions, and on the KITTI odometry dataset which is set in stereo configuration. The experiments demonstrated that the proposed method provides not only a practical but also robust SLAM solution for multi-camera systems.