Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM
This addresses the problem of resource limitations in visual SLAM applications, such as robotics and autonomous navigation, with an incremental improvement focused on back-end optimization.
The paper tackles the bottleneck of cost-efficiency in visual SLAM back-ends by introducing the Good Graph method for bundle adjustment, which improves accuracy and robustness under computational limits and enhances trajectory tracking in closed-loop navigation systems.
The cost-efficiency of visual(-inertial) SLAM (VSLAM) is a critical characteristic of resource-limited applications. While hardware and algorithm advances have been significantly improved the cost-efficiency of VSLAM front-ends, the cost-efficiency of VSLAM back-ends remains a bottleneck. This paper describes a novel, rigorous method to improve the cost-efficiency of local BA in a BA-based VSLAM back-end. An efficient algorithm, called Good Graph, is developed to select size-reduced graphs optimized in local BA with condition preservation. To better suit BA-based VSLAM back-ends, the Good Graph predicts future estimation needs, dynamically assigns an appropriate size budget, and selects a condition-maximized subgraph for BA estimation. Evaluations are conducted on two scenarios: 1) VSLAM as standalone process, and 2) VSLAM as part of closed-loop navigation system. Results from the first scenario show Good Graph improves accuracy and robustness of VSLAM estimation, when computational limits exist. Results from the second scenario, indicate that Good Graph benefits the trajectory tracking performance of VSLAM-based closed-loop navigation systems, which is a primary application of VSLAM.