Asynchronous Multicamera SLAM Using Sparse Gaussian Process Regression

Visual Simultaneous Localization and Mapping (VSLAM) is a key technique that enables autonomous systems to localize themselves and incrementally build a map of an unknown environment using only visual information. Despite its importance, conventional VSLAM systems frequently experience drift and tracking failures in complex environments, which limits their overall effectiveness. Multi-camera systems have enhanced VSLAM accuracy by incorporating diverse viewpoints. However, they generally rely on synchronous data capture, restricting their applicability in multi-sensor setups where asynchronous data acquisition is necessary. To address this limitation, a continuous-time Asynchronous Multi-Camera SLAM (AMC-SLAM) framework is proposed, utilizing sparse Gaussian process regression. The method integrates outlier removal, continuous-time trajectory optimization, and multi-view loop closing to achieve robust pose estimation. Combining Gaussian process interpolation with bundle adjustment strengthens inter-camera data correlation and reduces the number of state variables, while the derived analytical Jacobians enhance optimization efficiency. Additionally, online estimation of multi-camera extrinsic parameters is incorporated to improve the system’s generality. Experimental results on the AMV-Bench dataset [1] demonstrate an absolute translation error of less than 0.5% over a 10 km trajectory, indicating significant improvements in accuracy over existing stereo and multi-camera SLAM systems. The method also exhibits robustness and generalizability on the New College Dataset [2] and in real-world scenarios. These findings underscore the potential of AMC-SLAM for high-precision, robust applications in challenging environments.