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摘要:
基于单目视觉的同步定位与建图（SLAM）是机器人领域中的一项热门技术。然而，在场景建图方面，由于其计算量较大，各主流方法还无法在低运算能力的平台上实现实时的场景建模。针对室内环境与小型机器人的特定情况，提出了一种新的可通行区域建模方法。该方法建立在单目特征点SLAM的基础上，通过HSV色彩空间内的图像自适应阈值分割获取地面分割图像，并与SLAM生成的稀疏点云进行交叉比对，进而获取地平面与准确的地面分割区域，再将地面分割区域反投影到地平面上，获取地面的稠密建模。在室内场景的实验中，所提方法的平均运算速度能达到21帧/s，速度约为ORB-SLAM的70%，能够满足移动平台的实时性要求。对于地平面位置的还原平均误差为5.8%，地面上道路宽度的建模误差在3.5%~12.8%。
Abstract:Monocular vision-based simultaneous localization and mapping (SLAM) is a popular technology in the field of robotics in recent years. However, due to the huge computation resource required by reconstruction, mainstream methods are not able to generate meaningful reconstruction of scene in real time on platforms with low computing power. This paper proposes a new fast passable area modeling method for the specific situation of indoor environment and small robots. The method is based on the monocular feature-based SLAM. Firstly, it obtains the road segmentation image through segmentation in the HSV color space with adaptive threshold. Then, the system cross-matches the segmentation with the sparse point cloud generated by SLAM, to obtain the ground plane and accurate ground segmentation area. Finally, it projects the ground segmentation area to the ground plane for dense modeling of the floor. In the experiment of indoor scene, the average calculation speed of the proposed method can reach 21 frames per second, and the speed is about 70% of ORB-SLAM, which can meet the real-time requirements of mobile platforms. The position error for the floor plane is 5.8% on average, and the modeling error of the road width is between 3.5% and 12.8%.
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图 4 将分割图像投影到地平面对地面建模
Figure 4. Projecting segmented images to ground plane for ground modeling
图 7 本文方法得到的办公室稠密地面建模与非地面稀疏点云
Figure 7. Office dense ground modeling and non-ground sparse point cloud obtained by proposed method
图 10 本文方法得到的教室稠密地面建模与非地面稀疏点云
Figure 10. Classroom dense ground modeling and non-ground sparse point cloud obtained by proposed method
图 15 地面不均匀纹理对图像分割的影响
Figure 15. Effect of uneven texture of ground on image segmentation
表 1 平均每帧运算时间
Table 1. Average operation time per frame
模块 运算时间/ms 本文方法 ORB-SLAM SLAM进程 31.21 32.30 图像分割 5.16 — 地面点云获取 1.54 — 拟合平面 0.13 — 分割图像筛选 1.84 — 地面稠密建模 7.80 — 总计 47.68 32.30 
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表 2 地平面位置还原精度
Table 2. Accuracy of ground plane position restoration
场景 实际距离/cm 拟合地平面距离/cm 误差/% 办公室 42.0 43.6 3.81 教室 42.0 38.7 -7.86
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表 3 办公室地面建模精度
Table 3. Ground modeling accuracy of office
位置 实际距离/cm 模型距离/cm 误差/% a 135 129 4.4 b 112 108 3.5 c 128 116 10.3
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表 4 教室地面建模精度
Table 4. Ground modeling accuracy of classroom
位置 实际距离/cm 模型距离/cm 误差/% a 82 85 3.6 b 77 81 5.2 c 70 61 12.8
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