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.
A Novel Heading Angle Estimation Methodology for Land Vehicles Based on Deep Learning and Enhanced Digital Map