Light Detection and Ranging (LiDAR) sensors are popular in Simultaneous Localization and Mapping (SLAM) owing to their capability of obtaining ranging information actively. Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy. However, before employing LiDAR intensities in SLAM, a calibration operation is usually carried out so that the intensity is independent of the incident angle and range. The range is determined from the laser beam transmitting time. Therefore, the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface. In a complex environment, it is difficult to obtain the incident angle robustly. This procedure also complicates the data processing in SLAM and as a result, further application of the LiDAR intensity in SLAM is hampered. Motivated by this problem, in the present study, we propose a Hyperspectral LiDAR (HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM. HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements. Owing to the design of the laser, the eight-channel range and intensity were collected with the same incident angle and range. According to the laser beam radiation model, the ratio values between two randomly selected channels’ intensities at an identical target are independent of the range information and incident angle. To test the proposed method, the HSL was employed to scan a wall with different coloured papers pasted on it (white, red, yellow, pink, and green) at four distinct positions along a corridor (with an interval of 60 cm in between two consecutive positions). Then, a ratio value vector was constructed for each scan. The ratio value vectors between consecutive laser scans were employed to match the point cloud. A classic Iterative Closest Point (ICP) algorithm was employed to estimate the HSL motion using the range information from the matched point clouds. According to the test results, we found that pink and green papers were distinctive at 650, 690, and 720 nm. A ratio value vector was constructed using 650-nm spectral information against the reference channel. Furthermore, compared with the classic ICP using range information only, the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation. For the best case in the field test, the proposed method enhanced the heading angle estimation by 72%, and showed an average 25.5% improvement in a featureless spatial testing environment. The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.

中文翻译：

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一种利用多光谱激光雷达辅助SLAM中点云匹配的实用方法

光检测和测距（LiDAR）传感器由于能够主动获取测距信息的能力而在同时定位和制图（SLAM）中广受欢迎。研究人员已尝试使用每次距离测量随附的强度信息来增强LiDAR SLAM定位精度。但是，在SLAM中采用LiDAR强度之前，通常要执行校准操作，以使强度与入射角和入射范围无关。该范围由激光束透射时间确定。因此，在SLAM中使用LiDAR强度的关键是获得激光束与目标表面之间的入射角。在复杂的环境中，很难可靠地获得入射角。此过程还会使SLAM中的数据处理复杂化，结果，LiDAR强度在SLAM中的进一步应用受到阻碍。受此问题的影响，在本研究中，我们提出了一种基于高光谱LiDAR（HSL）的强度免校准方法，以帮助SLAM中的点云匹配。在这项研究中使用的HSL可以获得八通道范围，并伴有相应的强度测量值。由于激光器的设计，以相同的入射角度和范围收集了八通道的范围和强度。根据激光束辐射模型，在相同目标处两个随机选择的通道强度之间的比率值与范围信息和入射角无关。为了测试所提出的方法，我们使用HSL扫描墙壁上粘贴了不同颜色的纸张（白色，红色，黄色，粉红色，和绿色）沿着走廊的四个不同位置（两个连续位置之间的间隔为60厘米）。然后，为每次扫描构造一个比率值向量。连续激光扫描之间的比率值向量用于匹配点云。使用经典的迭代最近点（ICP）算法，使用来自匹配点云的范围信息来估计HSL运动。根据测试结果，我们发现粉红色和绿色的纸张在650、690和720 nm处有区别。使用针对参考通道的650 nm光谱信​​息构建比率值向量。此外，与仅使用距离信息的经典ICP相比，所提出的匹配比率值向量的方法在航向角估计中表现出了更高的性能。对于现场测试中的最佳情况，所提出的方法将航向角估计提高了72％，并且在无特征空间测试环境中显示出平均提高了25.5％。初步测试结果表明，该方法具有在实际场景的典型SLAM中辅助点云匹配的潜力。