Frosi Matteo, Bertoglio Riccardo, Matteucci Matteo
Dipartimento di Elettronica, Informazione e Bioingegneria of Politecnico di Milano, Milan, Italy.
Front Robot AI. 2023 Jan 24;10:1064930. doi: 10.3389/frobt.2023.1064930. eCollection 2023.
Positioning and navigation represent relevant topics in the field of robotics, due to their multiple applications in real-world scenarios, ranging from autonomous driving to harsh environment exploration. Despite localization in outdoor environments is generally achieved using a Global Navigation Satellite System (GNSS) receiver, global navigation satellite system-denied environments are typical of many situations, especially in indoor settings. Autonomous robots are commonly equipped with multiple sensors, including laser rangefinders, IMUs, and odometers, which can be used for mapping and localization, overcoming the need for global navigation satellite system data. In literature, almost no information can be found on the positioning accuracy and precision of 6 Degrees of Freedom Light Detection and Ranging (LiDAR) localization systems, especially for real-world scenarios. In this paper, we present a short review of state-of-the-art light detection and ranging localization methods in global navigation satellite system-denied environments, highlighting their advantages and disadvantages. Then, we evaluate two state-of-the-art Simultaneous Localization and Mapping (SLAM) systems able to also perform localization, one of which implemented by us. We benchmark these two algorithms on manually collected dataset, with the goal of providing an insight into their attainable precision in real-world scenarios. In particular, we present two experimental campaigns, one indoor and one outdoor, to measure the precision of these algorithms. After creating a map for each of the two environments, using the simultaneous localization and mapping part of the systems, we compute a custom localization error for multiple, different trajectories. Results show that the two algorithms are comparable in terms of precision, having a similar mean translation and rotation errors of about 0.01 m and 0.6°, respectively. Nevertheless, the system implemented by us has the advantage of being modular, customizable and able to achieve real-time performance.
由于定位和导航在现实世界场景中有多种应用，从自动驾驶到恶劣环境探索，因此它们是机器人技术领域的相关主题。尽管在室外环境中通常使用全球导航卫星系统（GNSS）接收器来实现定位，但在许多情况下，特别是在室内环境中，全球导航卫星系统无法使用的环境很常见。自主机器人通常配备有多个传感器，包括激光测距仪、惯性测量单元（IMU）和里程计，这些传感器可用于地图绘制和定位，从而无需全球导航卫星系统数据。在文献中，几乎找不到关于六自由度激光探测与测距（LiDAR）定位系统的定位精度和精确性的信息，尤其是在现实世界场景中。在本文中，我们对全球导航卫星系统无法使用的环境中的先进激光探测与测距定位方法进行了简要综述，突出了它们的优缺点。然后，我们评估了两种能够进行定位的先进同时定位与地图构建（SLAM）系统，其中一种是我们实现的。我们在手动收集的数据集上对这两种算法进行基准测试，目的是深入了解它们在现实世界场景中可达到的精度。特别是，我们进行了两项实验活动，一项在室内，一项在室外，以测量这些算法的精度。在使用系统的同时定位与地图构建部分为两个环境分别创建地图后，我们针对多个不同轨迹计算了自定义定位误差。结果表明，这两种算法在精度方面具有可比性，平均平移误差和旋转误差分别约为0.01米和0.6°，较为相似。然而，我们实现的系统具有模块化、可定制且能够实现实时性能的优势。
