RI:Small:Exploiting the Evolving Conditioning of Bundle Adjustment for Robust, Adaptive Simultaneous Localization and Mapping

同时定位和测绘(SLAM)是移动机器人在环境中建立其运动，同时恢复对局部环境几何形状的估计的过程。它是移动机器人感知处理子系统的重要组成部分，特别是在GPS不正确或不可用的情况下。该项目将开发技术来提高视觉SLAM算法的准确性、稳健性、可扩展性和效率。该项目将确定一种方法，以减少捆绑调整SLAM(BA-SLAM)算法的数据需求，同时提高或保持精度，从而产生能够用于移动机器人反馈回路的实时SLAM方法。将这些结果扩展到基于点和线的SLAM将增加SLAM适用和可靠的环境。该项目将促进本科生研究人员和团队的探索，并通过开放源代码发布代码来推动工程领域的研究。该项目还将通过演示利用这些发现实现的自主机器人活动来促进不同受众对工程的参与。这项研究将利用基于视觉的SLAM算法的时间限制特性来评估和提高健壮性，同时管理计算成本。当存在反馈环路时，SLAM的不确定性和延迟会对下游流程产生负面影响。新的SLAM修改将缓解这些问题，从而提供更准确和更低延迟的本地化和映射输出。修改的理论基础将依赖于可观测性理论、并发学习和矩阵理论。可观测性条件作用就SLAM中的估计适合性对输入数据进行评估。并发学习选择性地将时间间隔上的数据边际化，以提供用于捆绑调整的最小连通性推理图。矩阵理论建议高效、可扩展且近乎最佳的实现。对调查结果的验证将涉及现有基准和为测试开环和闭环系统运行而创建的新基准。该项目概括了通过补充数据评估和处理模块设计的所有视觉SLAM算法的积极影响，其作用是在提高或保持估计精度的同时降低估计延迟。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Simultaneous localization and mapping (SLAM) is the process by which a mobile robot establishes its movement through an environment while recovering an estimate of the local environment's geometry. It is an essential component of a mobile robot's sensory processing sub-system, especially when GPS is incorrect or unavailable. This project will develop technologies to improve the accuracy, robustness, scalability and efficiency of visual SLAM algorithms. The project will identify a means to reduce the data needs of bundle adjustment SLAM (BA-SLAM) algorithms while improving or preserving accuracy, leading to real-time SLAM methods capable of being used in the feedback loop of mobile robots. Extending these results for point and line-based SLAM will increase the environments within which SLAM is applicable and reliable. The project will promote exploration by undergraduate researchers and teams, and drive research in engineering fields through open source release of the code. The project will also promote engagement in engineering by diverse audiences through demonstrations using autonomous robotic activities enabled by the findings. This research will exploit the temporal conditioning properties of vision-based SLAM algorithms for assessing and improving robustness while managing computational cost. SLAM uncertainty and latency negatively impacts downstream processes when there is a feedback loop. New SLAM modifications will mitigate these issues, thereby providing more accurate and lower latency localization and mapping outputs. The theoretical foundations for the modifications will rely on observability theory, concurrent learning, and matrix theory. Observability conditioning assesses the incoming data with regards to estimation fitness in SLAM. Concurrent learning selectively marginalizes data over time intervals to provide a minimal connectivity inference graph for bundle adjustment. Matrix theory suggests efficient, scalable, and nearly optimal implementations. Validation of the findings will involve existing benchmarks and new benchmarks created to test open and closed loop operation. The project generalizes to positively impact all visual SLAM algorithms designed to date through complementary data assessment and processing blocks, whose role is to lower estimation latency while improving or preserving estimation accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.