Mou Xiaozheng, Wang Han
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel). 2018 Apr 4;18(4):1085. doi: 10.3390/s18041085.
This paper proposes a wide-baseline stereo-based static obstacle mapping approach for unmanned surface vehicles (USVs). The proposed approach eliminates the complicated calibration work and the bulky rig in our previous binocular stereo system, and raises the ranging ability from 500 to 1000 m with a even larger baseline obtained from the motion of USVs. Integrating a monocular camera with GPS and compass information in this proposed system, the world locations of the detected static obstacles are reconstructed while the USV is traveling, and an obstacle map is then built. To achieve more accurate and robust performance, multiple pairs of frames are leveraged to synthesize the final reconstruction results in a weighting model. Experimental results based on our own dataset demonstrate the high efficiency of our system. To the best of our knowledge, we are the first to address the task of wide-baseline stereo-based obstacle mapping in a maritime environment.
本文提出了一种基于宽基线立体视觉的无人水面艇（USV）静态障碍物映射方法。该方法省去了我们之前双目立体视觉系统中复杂的校准工作和庞大的设备，并通过USV的运动获得更大的基线，将测距能力从500米提高到1000米。在该系统中集成单目相机与GPS和罗盘信息，在USV行驶时重建检测到的静态障碍物的世界位置，然后构建障碍物地图。为了实现更准确和稳健的性能，利用多对帧在加权模型中合成最终的重建结果。基于我们自己数据集的实验结果证明了我们系统的高效性。据我们所知，我们是首个解决在海洋环境中基于宽基线立体视觉的障碍物映射任务的。
