Low-cost camera and 2-D LIDAR fusion for target vehicle corner detection and tracking: Applications to micromobility devices
Low-cost camera and 2-D LIDAR fusion for target vehicle corner detection and tracking: Applications to micromobility devices
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DOI：
10.1016/j.ymssp.2023.110891
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发表时间：
2024-01
影响因子：
8.4
通讯作者：
Hamidreza Alai;R. Rajamani
中科院分区：
文献类型：
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作者：
Hamidreza Alai;R. Rajamani
本文开发了一种基于二维LIDAR和单目摄像机融合的高性价比车辆检测和跟踪系统，以通过实时预测汽车与滑板车的碰撞危险来保护电动微动设备，特别是电动滑板车。三维激光雷达传感器的成本和尺寸缺点使其不适合微移动设备。因此，使用了2-D RPLIDAR Mapper传感器。这种传感器虽然成本低，但存在垂直视场窄、数据点密度低等主要缺点。由于这些因素，传感器在户外应用中输出不稳定，测量结果在车辆表面上不断跳跃和滑动。为了提高LIDAR的性能，将单目摄像机与LIDAR数据融合，不仅可以检测车辆，还可以分别检测目标车辆的正面和侧面，并找到目标车辆的拐角。实验结果表明，该角点检测方法比单纯基于LIDAR数据的角点检测方法更准确。角点测量在高增益观测器中用于估计目标车辆的位置、速度和方向。所开发的系统在Ninebot电动滑板车平台上实现，并进行了多个实验来评估算法的性能。
This paper develops a cost-effective vehicle detection and tracking system based on fusion of a 2-D LIDAR and a monocular camera to protect electric micromobility devices, especially e-scooters, by predicting the real- time danger of a car- scooter collision. The cost and size disadvantages of 3-D LIDAR sensors make them an unsuitable choice for micromobility devices. Therefore, a 2-D RPLIDAR Mapper sensor is used. Although low-cost, this sensor comes with major shortcomings such as the narrow vertical field of view and its low density of data points. Due to these factors, the sensor does not have a robust output in outdoor applications, and the measurements keep jumping and sliding on the vehicle surface. To improve the performance of the LIDAR, a single monocular camera is fused with the LIDAR data not only to detect vehicles, but also to separately detect the front and side of a target vehicle and to find its corner. It is shown that this corner detection method is more accurate than strategies that are only based on the LIDAR data. The corner measurements are used in a high-gain observer to estimate the location, velocity, and orientation of the target vehicle. The developed system is implemented on a Ninebot e-scooter platform, and multiple experiments are performed to evaluate the performance of the algorithm.