Abstract This paper addresses the problem of obtaining an Earth-fixed trajectory and map (ETM), with the associated uncertainty, using the sensor-based map provided by a globally asymptotically/exponentially stable (GES) SLAM filter. The algorithm builds on an optimization problem with a closed-form solution, and its uncertainty description is derived resorting to perturbation theory. The combination of the algorithm proposed in this paper with sensor-based SLAM filtering results in a complete SLAM methodology, which is directly applied to the three main different formulations: range-and-bearing, range-only, and bearing-only. Simulation and experimental results for all these formulations are included in this work to illustrate the performance of the proposed algorithm under realistic conditions. The ETM algorithm proposed in this paper is truly sensor-agnostic, as it only requires a sensor-based map and imposes no constraints on how this map is acquired nor how egomotion is captured. However, in the experiments presented herein, all the sensor-based filters use a sensor to measure the angular velocity and, for the range-only and bearing-only formulations, a sensor to measure the linear velocity.

中文翻译：

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地球固定轨迹和地图在线估计：建立在基于 GES 传感器的 SLAM 滤波器之上

摘要 本文解决了使用全局渐近/指数稳定 (GES) SLAM 滤波器提供的基于传感器的地图获得具有相关不确定性的地球固定轨迹和地图 (ETM) 的问题。该算法建立在具有封闭形式解的优化问题上，其不确定性描述是借助微扰理论推导出来的。本文提出的算法与基于传感器的 SLAM 过滤相结合，形成了完整的 SLAM 方法，该方法直接应用于三种主要不同的公式：距离和方位、仅距离和仅方位。所有这些公式的模拟和实验结果都包含在这项工作中，以说明所提出算法在现实条件下的性能。本文中提出的 ETM 算法真正与传感器无关，因为它只需要一个基于传感器的地图，并且对如何获取该地图或如何捕获自我运动没有任何限制。然而，在此处介绍的实验中，所有基于传感器的滤波器都使用传感器来测量角速度，对于仅测距和仅轴承的公式，使用传感器来测量线速度。