论文标题
优化的好图表：视觉大满贯中具有成本效益的，预算吸引的捆绑包调整
Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM
论文作者
论文摘要
视觉（ - 内部）大满贯（VSLAM）的成本效率是资源有限应用的关键特征。尽管硬件和算法的进步已大大提高了VSLAM前端的成本效益，但VSLAM后端的成本效率仍然是一种瓶颈。本文描述了一种新颖的严格方法，可在基于BA的VSLAM后端提高本地BA的成本效益。开发了一种称为“良好图”的有效算法，以选择具有条件保存的本地BA中优化的尺寸还原图。为了更好地适合基于BA的VSLAM后端，良好的图表可以预测未来的估计需求，动态分配适当的尺寸预算，并选择一个条件最大的子图以进行BA估计。评估是在两种情况下进行的：1）VSLAM作为独立过程，而2）VSLAM作为闭环导航系统的一部分。从第一个方案出发的结果表明，当存在计算限制时，良好的图可提高VSLAM估计的准确性和鲁棒性。第二种情况的结果表明，良好的图会受益于基于VSLAM的闭环导航系统的轨迹跟踪性能，这是VSLAM的主要应用。
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.
