CamMap: Extrinsic Calibration of Non-Overlapping Cameras Based on SLAM Map Alignment
Jie Xu, Ruifeng Li, Lijun Zhao, Wenlu Yu, Zhiheng Liu, Bo Zhang, Yuchen Li
Abstract
Multiple cameras have emerged as a promising technology for robots and vehicles due to their broad fields of view (FoV) and high resolution. However, there are often limited or no overlapping FoVs among cameras, bringing challenges to estimating extrinsic camera parameters. To overcome this problem, we propose CamMap: a novel 6-degree-of-freedom (DoF) extrinsic calibration pipeline. Following three operating rules, we make a multi-camera rig capture some similar image sequences individually to create sparse feature-based maps with a SLAM system. A two-stage optimization problem is formulated to align the maps and obtain the transformations between them based on bidirectional reprojection. The transformations are exactly the extrinsic parameters. Supporting diverse camera types, the pipeline is available in any texture-rich environment. It can calibrate any number of cameras simultaneously without requiring calibration patterns, synchronization, same resolution and frequency. The pipeline is evaluated on cameras with limited and no overlapping FoVs. In the experiments, we demonstrate our method's accuracy and efficiency. The absolute pose error (APE) between Kalibr and CamMap is less than 0.025.