Imaging lidar based 3D terrain matching using feature vector

Hua Cheng; Jie Ma; Junbin Gong

doi:10.1117/12.749181

15 November 2007 Imaging lidar based 3D terrain matching using feature vector

Hua Cheng, Jie Ma, Junbin Gong

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

Abstract

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.

Citation Download Citation

Hua Cheng, Jie Ma, and Junbin Gong "Imaging lidar based 3D terrain matching using feature vector", Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 678715 (15 November 2007); https://doi.org/10.1117/12.749181

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KEYWORDS

Scanning transmission electron microscopy

LIDAR

Detection and tracking algorithms

Ranging

Transmission electron microscopy

3D image processing

Error analysis

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