15 November 2007 Imaging lidar based 3D terrain matching using feature vector
Author Affiliations +
Proceedings Volume 6787, MIPPR 2007: Multispectral Image Processing; 678715 (2007) https://doi.org/10.1117/12.749181
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
A novel 3D terrain matching algorithm is presented in this paper. A terrain feature vector map (FVM), composed of local mean and local gradient, is employed to represent the terrain elevation map (TEM). Compared with traditional matching algorithm using the magnitude of gradient to match, the new algorithm uses each component of the gradient vector to match individually, and it is able to generate two interim matching positions. Different from traditional matching algorithms which usually estimate an optimum matching position under some criterions at the end, the new algorithm fused the two interim matching positions to generate a final matching position or refuse to position in order to increase the matching confidence, which is very important because it is hardly acceptable to employ a mismatched position to correct the error of Inertial Navigation System (INS). Due to the stability of terrain and the high-precision of lidar ranging, the mean of a sensed terrain elevation map (STEM) sized terrain is quite stable. So it is bestowed to accelerate the matching process and to reduce mismatches at different terrain heights. Compared with other mismatch-eliminated methods based on neural network (NN) or support vector machine (SVM), the new method do not need training samples and is more stable and robust. Experimental results show that the proposed algorithm is effective and robust.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hua Cheng, Hua Cheng, Jie Ma, Jie Ma, Junbin Gong, Junbin Gong, } "Imaging lidar based 3D terrain matching using feature vector", Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 678715 (15 November 2007); doi: 10.1117/12.749181; https://doi.org/10.1117/12.749181

Back to Top