Simultaneous Localization and Mapping (SLAM) is a technology developed to overcome the problems of robot localization and navigation. This paper uses a simple and low-cost way to establish indoor environment information map data for work planning and patrol monitoring of self-propelled robots. The advanced mobile robot uses mainly Lidar and camera to sense the environment. Being of its high accuracy and large sensing range, the robot gets a large amount of environment sensing data, use it on SLAM to obtain a detailed environment map, and locate the robot location. However, in order for the robot to use Lidar and camera to detect environmental data, it must use higher-level controller and memory to deal with a large number of sensing data, resulting in an increase in the price of sensors and the cost of matching hardware. In this paper, a low-cost single line Lidar, combined with DC motor and rotary encoder, is installed on a small robot, so that it obtains the data of different measurement points, and the robot is used as the test vehicle to explore the indoor environment. The experimental result shows that its sensing characteristics are sensitive, and the sensing data meet the requirements of real-time mapping.
This paper uses the point cloud data extracted by LiDAR as the basis of obstacle detection and proposes an algorithm to reduce the computational complexity of point cloud. Firstly, it removes the points too high or too low from the point cloud data, leaving only the points forming the obstacle interval. Secondly, the point cloud in the interval is projected to the X-Y plane (horizontal plane) and the resolution is reduced by lattice. Finally, the connected component marker clustering method is used to segment and classify obstacles. After identifying the obstacle, the scene information is provided to the path planning algorithm. In the path planning algorithm, Unmanned Ground Vehicle (UGV) moves along the shortest straight line connecting the target point and the starting point. When encountering obstacles, the edge tracking method is used to bypass the obstacle, and then it goes straight again. After planning by the path planning algorithm, UGV can avoid collision and advance to the destination in real time through the motion control system.
The experimental result shows that UGV designed by this research has the ability to reach the target point safely, and it can avoid obstacles and move autonomously in real-time conditions and complete outdoor unknown environmental obstacle detection.
In recent years, the research of intelligent transportation system has become more and more popular. Whether it was the environmental information around the vehicle, the landmarks on the road or the position of the vehicle itself. It played a very important role in the intelligent traffic control of autonomous mobile intelligent vehicle or vehicle driving assistance system. This paper analyzes the 3D point cloud data collecting and constructing by the radar to obtain the location and distance between the self-propelled vehicle and the road marking in the real environment space. In the part of detecting the road edge, it uses the characteristics of the height difference between the driving road and the sidewalk or fence, as well as the distance between two points in point cloud data and gradient filter to divide road area and non-Road area. In order to obtain more complete and accurate environmental data, the non-road data segmented uses to splice the feature points of the continuous picture through the previous method, and put calculated conversion matrix into the road area to obtain two kinds of information: the non-driving road environment map and the feasible driving road area map. In the 3D map information of drivable pavement area, the lane markings may be extracted from the asphalt pavement and road markings according to the different of LiDAR reflection intensity. Then the road edge detected on the point cloud image is projected onto the 2D color image. The detection and tracking of lane markings on the road area image are realized by image processing method, and the experiment results show that the marking machine or self-propelled vehicle can move along the lane marking accurately.
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