Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter; first with adaptive noise tuning and second with Chi-squared test for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control.

翻译：全球导航卫星系统在室外机器人定位中能可靠地提供精确位置信息。然而，对于在室内运行的移动机器人，目前尚无类似的标准解决方案。本文提出了一种基于先进驾驶辅助系统（ADAS）模型车的室内定位集成框架及其自动驾驶系统的实验验证方法。通过融合基准标记、惯性传感器和车轮里程计信息来获取模型车的全局位姿。为实现鲁棒定位，我们研究并比较了扩展卡尔曼滤波的两种扩展方法：第一种采用自适应噪声调节，第二种利用卡方检验进行测量异常值检测。还提出了一种利用单激光雷达传感器的高效低成本真值测量方法来验证结果。定位算法的性能在包含轨迹规划和模型预测控制的完整自动驾驶系统上进行了测试。