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Online glass confidence map building using laser rangefinder for mobile robots
Advanced Robotics ( IF 2 ) Pub Date : 2020-09-16 , DOI: 10.1080/01691864.2020.1819873
Jun Jiang 1 , Renato Miyagusuku 2 , Atsushi Yamashita 1 , Hajime Asama 1
Affiliation  

Accurate localization and mapping are essential for mobile robots. Using laser rangefinders (LRFs), current state-of-the-art indoor Simultaneous Localization and Mapping (SLAM) can provide accurate real-time localization and mapping in most environments. An exemption are those where glass is predominant, as LRFs can not properly detect glass due to glass' transparency and reflectiveness. With such buildings becoming more common, this has become an important issue to address. Failure to detect glass causes two problems for SLAM: incorrectly mapping glass as open space; and, lower localization accuracy due to mismatches between measured and expected range data. In this paper, we propose a glass confidence map that correctly maps glass as occupied, as well as the probability of an object to be glass/non-glass. Our approach consists of four steps: (i) map all objects, even potential dynamic obstacles, as occupied, (ii) compute the probability of scanned objects to be glass/non-glass using a neural network, (iii) online map updates by matching scanned objects to probability map, and (iv) filter dynamic obstacles and noise. We validated our approach in an office with large glass areas, achieving more than 95% of glass areas correctly mapped as occupied with less than 5% glass/non-glass classification error. GRAPHICAL ABSTRACT

中文翻译:

使用激光测距仪为移动机器人构建在线玻璃置信度图

准确的定位和映射对于移动机器人至关重要。使用激光测距仪 (LRF),当前最先进的室内同步定位和测绘 (SLAM) 可以在大多数环境中提供准确的实时定位和测绘。例外情况是玻璃占主导地位的情况,因为由于玻璃的透明度和反射性,LRF 无法正确检测玻璃。随着此类建筑物变得越来越普遍,这已成为需要解决的重要问题。未能检测到玻璃会导致 SLAM 出现两个问题:错误地将玻璃映射为开放空间;并且,由于测量和预期范围数据之间的不匹配导致定位精度降低。在本文中,我们提出了一种玻璃置信度图,可以正确地将玻璃映射为被占用,以及物体是玻璃/非玻璃的概率。我们的方法包括四个步骤:(i) 映射所有物体,甚至是潜在的动态障碍物,(ii) 使用神经网络计算扫描物体是玻璃/非玻璃的概率,(iii) 通过将扫描物体与概率图匹配来在线更新地图, (iv) 过滤动态障碍物和噪音。我们在具有大玻璃区域的办公室中验证了我们的方法,实现了 95% 以上的玻璃区域正确映射为占用,玻璃/非玻璃分类错误小于 5%。图形概要 实现 95% 以上的玻璃区域正确映射为占用,玻璃/非玻璃分类错误小于 5%。图形概要 实现 95% 以上的玻璃区域正确映射为占用,玻璃/非玻璃分类错误小于 5%。图形概要
更新日期:2020-09-16
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