Robust Initialization of Multi-camera SLAM with Limited View Overlaps and Inaccurate Extrinsic Calibration

Abstract

This paper proposes a robust initialization method for a multi-camera visual SLAM system where cameras have only a limited common field of views and inaccurate extrinsic calibration. The limited common field of views leads to only a few common features that can be matched between cameras. Inaccurate extrinsic poses, caused by vibrations or misplacement of cameras after offline calibration, make it even harder for triangulating the seed 3D points to initialize the SLAM system successfully. Instead of taking the extrinsic parameters as constants for feature matching and 3D point triangulation as most multi-camera systems did, we propose to take the inaccurate extrinsic poses as soft constraints to accommodate the calibration errors. Our initialization method consists of two stages by matching across different cameras and between two key frames. Both stages involve optimizing the cost functions that contain the extrinsic pose priors from inaccurate calibration parameters. By incorporating those soft pose constraints, we may avoid false feature matching and triangulation caused by inaccurate extrinsic parameters, while keeping solution space limited when only a few feature correspondences exist. The results in real-world tests show that such a simple solution can improve the success rate of SLAM initialization notably, even when the pose priors from the offline calibration differ significantly from the real ones.