Abstract
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.
Highlights
Light Detection and Ranging (LiDAR) sensors are active devices that obtain range information; they have been extensively employed in Simultaneous Localization and Mapping (SLAM) applications (Qian et al 2017; Chen et al 2018a, b, c; Tang et al 2015)
This paper presented a new method utilizing LiDAR intensities to aid in point cloud matching
A ratio value was defined based on multispectral information to exclude the influence of the range and incident angle on the LiDAR intensities
Summary
LiDAR sensors are active devices that obtain range information; they have been extensively employed in SLAM applications (Qian et al 2017; Chen et al 2018a, b, c; Tang et al 2015). Range information is obtained by measuring the time of flight between the emitted pulse and the reflected laser echoes from targets. The intensity information that accompanies the range information refers to the power strength of the reflected laser echoes (Guivant et al 2000; Yoshitaka et al 2006). Researchers attempted to leverage the one-channel intensity information to enhance LiDAR SLAM positioning accuracy (Wolcott and Eustice 2015; Barfoot et al 2016; Hewitt and Marshall 2015). The intensity is only obtained from a single spectral wavelength, which is insufficient for feature extraction and certain target classification. Additional spectral information or channels are preferable for this application
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