Abstract
Despite the number of works published in recent years, vehicle localization remains an open, challenging problem. While map-based localization and SLAM algorithms are getting better and better, they remain a single point of failure in typical localization pipelines. This paper proposes a modular localization architecture that fuses sensor measurements with the outputs of off-the-shelf localization algorithms. The fusion filter estimates model uncertainties to improve odometry in case absolute pose measurements are lost entirely. The architecture is validated experimentally on a real robot navigating autonomously proving a reduction of the position error of more than 90% with respect to the odometrical estimate without uncertainty estimation in a two-minute navigation period without position measurements.
Abstract (translated)
尽管近年来发表的作品数量不断增加，但车辆定位仍然是一个开放且具有挑战性的问题。虽然基于地图的定位和SLAM算法正在越来越好，但它们在典型的定位流程中仍然是一个单点故障。本文提出了一种模块化的定位架构，将传感器测量结果与普通定位算法的输出相结合。融合滤波器估计模型不确定性以提高在没有绝对姿态测量的情况下进行逆向推理的里程计误差。实验验证表明，该架构在自主导航的机器人上实现了超过90%的定位误差减少，而在没有位置测量的情况下，绝对姿态测量的误差估计时间不到两分钟。
URL
https://arxiv.org/abs/2403.13452
