Abstract
The registration of optical imagery and 3D Light Detection and Ranging (LiDAR) point data continues to be a challenge for various applications in photogrammetry and remote sensing. In this paper, the framework employs a new registration primitive called virtual point (VP) that can be generated from the linear features within a LiDAR dataset including straight lines (SL) and curved lines (CL). By using an auxiliary parameter (λ), it is easy to take advantage of the accurate and fast calculation of the one-step registration transformation model. The transformation model parameters and λs can be calculated simultaneously by applying the least square method recursively. In urban areas, there are many buildings with different shapes. Therefore, the boundaries of buildings provide a large number of SL and CL features and selecting properly linear features and transforming into VPs can reduce the errors caused by the semi-discrete random characteristics of the LiDAR points. According to the result shown in the paper, the registration precision can reach the 1~2 pixels level of the optical images.
Highlights
Light Detection and Ranging (LiDAR) and airborne image data for three different sites are used to verify the validity of the algorithm by using it for different regions, as Table 3 shows: an urban region, a rural region, and a mixed region
We used the linear feature and curve feature to test the registration method described in this paper and compared the registration accuracy of the two features
This paper proposes a novel method for registration of the two datasets acquired in cloud data is one of the reasons for the decline of data accuracy, and the registration urban scenes by using a direct transformation function and virtual point (VP) registration primitives that can be generated from the straight line or curve features as control information
Summary
In Bai Zhu’s method, the intensity image sampling from the LiDAR point. This paper proposes a novel method for registration of the two datasets acquired in cloud data is one of the reasons for the decline of data accuracy, and the registration urban scenes by using a direct transformation function and VP registration primitives that can be generated from the straight line or curve features as control information. In Bisheng Yang’s method, the image and LiDAR point data are collected by the unmanned aerial vehicle, equipped with a poor POS system, and that will lead to poor registration accuracy. The method of this paper is suitable for both the single and multiple images situation to registration with the LiDAR points. In Bisheng Yang’s method, it is only suitable for multiple image registration with
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