Improvement of Lidar Data Accuracy Using Lidar-Specific Ground Targets

With recent advances of lidar technology, the accuracy potential of lidar data has significantly improved. State-ofthe-art lidar systems can achieve 2 to 3 cm ranging accuracy under ideal conditions, which is the accuracy level required by engineering scale mapping. However, this is also the accuracy range that cannot be realized by routine navigation-based direct sensor platform orientation. Furthermore, lidar systems are highly integrated multi-sensor systems, and the various components, as well as their spatial relationships, introduce different errors that can degrade the lidar data accuracy. Even after careful system calibration, including individual sensor calibration and sensors intra-calibration, certain errors in the collected data can still be present. These errors are usually dominated by navigation errors and cannot be totally eliminated without introducing absolute control information into the lidar data. Therefore, to support applications that require extremely high, engineering scale mapping accuracy, such as transportation corridor mapping, we propose the use of lidar-specific ground targets. Simulations were performed to determine the most advantageous lidar target design and targets were fabricated based upon the simulation results. To investigate the potential of using control targets for lidar data refinement, test flights were carried out with different flight parameters and target distributions. This paper provides a description of the optimal lidar target design, the target identification algorithm, and a detailed performance analysis, including the investigation of the achievable lidar data accuracy improvement using lidar-specific ground control targets in the case of various target distributions and flight parameters. Introduction Lidar systems have advanced considerably in recent years. In particular, the pulse rate frequency has increased significantly, the ranging accuracy has improved, and the availability of intensity signal has become common (Toth, 2004). These developments have resulted in better data quality in terms of higher point density and better accuracy, which, in Improvement of Lidar Data Accuracy Using Lidar-Specific Ground Targets [THIS PAPER WAS THE WINNER OF THE 2005 BAE SYSTEMS AWARD GIVEN AT THE ASPRS 2005 ANNUAL CONFERENCE] Nora Csanyi and Charles K. Toth turn, further widened the already broad application field of laser scanning (Renslow, 2005). For example, the centimeterlevel range measurement accuracy could, in theory, support engineering scale mapping for the first time. However, this accuracy range can be achieved only for specific landscapes with good reflective characteristics and simple geometric features. Highway corridors contain mostly man-made objects with flat smooth surfaces and near-uniform reflectivity, and are usually free of vegetation thus holding the potential that the laser ranging accuracy can be approached. Of course, not all applications demand such a high accuracy. The difficulty of achieving centimeter-level accuracy, however, goes beyond laser ranging accuracy and landscape dependency, as there are many other factors in the error budget of an airborne lidar system. Lidar systems are complex multi-sensory systems and incorporate at least three main sensors: GPS and INS navigation sensors, and the laser-scanning device. Furthermore, there is a moving component with the usual problems of position encoding, wear, and mechanical hysteresis that can further degrade the accuracy of the acquired lidar data. In general, the errors in laser scanning data can come from individual sensor calibration or measurement errors, lack of synchronization, or misalignment between the different sensors. Baltsavias (1999) presents an overview of basic relations and error formulae concerning airborne laser scanning. Even after careful system calibration, some errors could be present in the data, and navigation errors usually dominate. The errors become evident as discrepancies between overlapping strips and at ground control surfaces. Most of the systematic errors can be corrected by strip adjustment (with or without ground control) by eliminating the discrepancies between overlapping lidar strips. In the last few years, various strip adjustment methods have been developed. Several strip adjustment methods minimize only the vertical discrepancies between overlapping strips or between strips and horizontal control surfaces. These strip adjustments can be referred as one-dimensional strip adjustment methods (Crombaghs et al., 2000; Kager and Kraus, 2001). Tie or absolute control features used for this adjustment are flat horizontal surfaces. The problem with this kind of adjustment is that existing planimetric errors are likely to remain in the data. Vosselman and Maas (2001) have shown PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Ap r i l 2007 385 Nora Csanyi is with The Ohio State University, Department of Civil and Environmental Engineering and Geodetic Science, 2070 Neil Avenue, Columbus, OH 43210 and The Center for Mapping, 1216 Kinnear Road, Columbus, OH 43212 ([email protected]). Charles K. Toth is with The Ohio State University, The Center for Mapping, 1216 Kinnear Road, Columbus, OH 43212 ([email protected]). Photogrammetric Engineering & Remote Sensing Vol. 73, No. 4, April 2007, pp. 385–396. 0099-1112/07/7304–0385/$3.00/0 © 2007 American Society for Photogrammetry and Remote Sensing 05-137 07/03/2006 11:04 AM Page 385