Huang Hao, Hu Yongbiao, Wang Xuebin
National Engineering Laboratory for Highway Maintenance Equipment, Chang'an University, Xi'an 710064, China.
Sensors (Basel). 2024 Jul 7;24(13):4408. doi: 10.3390/s24134408.
As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels.
作为道路施工中的重要设备，无人驾驶压路机在自主压实能力方面正迅速发展。为了克服隧道施工期间全球导航卫星系统（GNSS）定位失败以及在不同光照水平下视觉定位精度降低的挑战，我们提出了一种基于相机和激光雷达的特征层融合定位系统。该系统将回环检测和激光雷达里程计集成到视觉里程计框架中。此外，认识到隧道中相似场景的普遍性，我们创新性地将回环检测与压路机在固定区域的压实过程相结合，提出了一种基于压实过程的回环候选帧选择方法。通过现场实验表明，该方法不仅提高了相似环境下回环检测的准确性，还减少了运行时间。与视觉系统相比，在静态定位测试中，融合系统的纵向和横向精度分别提高了12毫米和11毫米。在不同光照水平下的直线压实测试中，平均横向误差分别增加了34.1%和32.8%。在变道压实测试中，该系统在昏暗环境下将定位精度提高了33%，证明了融合定位系统在隧道光照变化情况下具有卓越的定位精度。
