Technology Description
Mobile LiDAR systems are used in a variety of applications such as mapping and autonomous vehicles. A fundamental part of mobile systems is deriving the trajectory of the LiDAR unit as it is moving. Many LiDAR systems do not capture this data requiring it to be reconstructed. This method for trajectory reconstruction offers the ability to reconstruct the trajectory data from point cloud data alone. This is useful because the ability to reconstruct trajectories allows systems to do much more accurate feature extraction, segmentation, and classification. One can also use this data to derive the accuracy of the points in the point cloud itself, again allowing for further refinement and accuracy of any software that utilizes the point cloud. The method is robust and fast and as a secondary benefit this method also allows one to manage and classify LiDAR data with ease. This method for trajectory reconstruction can be applied to any 2D profiler-based system. This gives the method a broad range of applications in the LiDAR market. The sectors that stand to benefit most are autonomous vehicles and mapping projects. This could be quite significant given that the autonomous vehicles market has huge potential to grow. Furthermore, larges automakers pursuing autonomous vehicles have announced that they will be using LiDAR systems, making trajectory reconstruction technology important to the industry. The key difficulty of enhancing products that require trajectory information is that many LiDAR systems do not record this data, and many methods for constructing it are not as comprehensive, accurate, or fast. Because of this the potential of this reconstruction system to offer value to the market is large.
Features & Benefits
This technology was produced over the course of three years of research by the School of Civil and Construction Engineering at Oregon State University by Michael Olsen, Associate Professor of Geomatics and Editor-in-Chief for the ASCE Journal of Surveying Engineering; and Erzhuo Che, postdoc at OSU. Development was supported by the National Science Foundation and Oregon Department of Transportation (ODOT).
U.S. patent pending; available for licensing.