  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.
