在本文中,提出了一种基于2DLiDAR和RGB-D相机传感的智能导盲系统,系统安装在智能手杖上。智能引导系统依赖于2D激光雷达,RGB-D相机,IMU,GPS,JetsonnanoB01、STM32等硬件。我们提出的智能引导系统的主要优点是智能手杖与障碍物之间的距离可以通过基于制图师算法的2DLiDAR来测量,从而实现同时定位和映射(SLAM)。同时,通过改进的YOLOV5算法,行人,车辆,人行道,交通灯,警告柱,石墩,触觉铺路,可以快速有效地识别视觉障碍面前的其他物体。激光SLAM和改进的YOLOv5障碍物识别测试是在海南师范大学校园内的教学楼内和海口市龙昆南路的人行道口进行的,海南省。结果表明,我们开发的智能引导系统可以驱动智能手杖底部的全向轮,并为智能手杖提供自引领的盲引导功能,就像一只“导盲犬”,这可以有效地引导视障人士避开障碍物并到达预定目的地,并能快速有效地识别出出路的障碍。该系统的激光SLAM的映射和定位精度为1m±7cm,该系统的激光SLAM速度为25〜31FPS,在室内和室外环境中都能实现短距离避障和导航功能。改进的YOLOv5有助于识别86种对象。行人人行横道和车辆的识别率分别为84.6%和71.8%,对86种物体的总体识别率分别为61.2%,智能导游系统的障碍物识别率为25-26FPS。
In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on a smart cane. The intelligent guide system relies on 2D LiDAR, an RGB-D camera, IMU, GPS, Jetson nano B01, STM32, and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on the
cartographer algorithm, thus achieving simultaneous localization and mapping (SLAM). At the same time, through the improved YOLOv5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving, and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved YOLOv5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the omnidirectional wheels at the bottom of the smart cane and provide the smart cane with a self-leading blind guide function, like a \"guide dog\", which can effectively guide the visually impaired to avoid obstacles and reach their predetermined destination, and can quickly and effectively identify the obstacles on the way out. The mapping and positioning accuracy of the system\'s laser SLAM is 1 m ± 7 cm, and the laser SLAM speed of this system is 25~31 FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor environments. The improved YOLOv5 helps to identify 86 types of objects. The recognition rates for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25-26 FPS.