10 October 2017 Analysis of the SNR and sensing ability of different sensor types in a LIDAR system
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Abstract
LIDAR (light distance and ranging) systems use sensors to detect reflected signals. The performance of the sensors significantly affects the specification of the LIDAR system. Especially, the number and size of the sensors determine the FOV (field of view) and resolution of the system, regardless of which sensors are used. The resolution of an array-type sensor normally depends on the number of pixels in the array. In this type of sensor, there are several limitations to increase the number of pixels in an array for higher resolution, specifically complexity, cost, and size limitations. Another type of sensors uses multiple pairs of transmitter and receiver channels. Each channel detects different points with the corresponding directions indicated by the laser points of each channel. In this case, in order to increase the resolution, it is required to increase the number of channels, resulting in bigger sensor head size and deteriorated reliability due to heavy rotating head module containing all the pairs. In this paper, we present a method to overcome these limitations and improve the performance of the LIDAR system. ETRI developed a type of scanning LIDAR system called a STUD (static unitary detector) LIDAR system. It was developed to solve the problems associated with the aforementioned sensors. The STUD LIDAR system can use a variety of sensors without any limitations on the size or number of sensors, unlike other LIDAR systems. Since it provides optimal performance in terms of range and resolution, the detailed analysis was conducted in the STUD LIDAR system by applying different sensor type to have improved sensing performance.
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Gyudong Choi, Gyudong Choi, Munhyun Han, Munhyun Han, Hongseok Seo, Hongseok Seo, Bongki Mheen, Bongki Mheen, } "Analysis of the SNR and sensing ability of different sensor types in a LIDAR system", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104271S (10 October 2017); doi: 10.1117/12.2278401; https://doi.org/10.1117/12.2278401
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