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Data processing approaches on SPAD-based flash LiDAR systems. A review

 
: Chen, Gongbo; Wiede, Christian; Kokozinski, Rainer

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Volltext ()

IEEE Sensors Journal (2020), Online First, 11 S.
ISSN: 1530-437X
ISSN: 1558-1748
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IMS ()
advanced driver assistance systems (ADAS); data processing; light detection and ranging (LIDAR); point cloud; Time-of-Flight (ToF); time-correlated histograms

Abstract
With the rise of advanced driver assistance systems (ADAS), range sensors and their data processing methods are becoming more and more important. Light detection and ranging (LiDAR) sensors are attracting attention due to their unique advantages in terms of radial distance resolution and detection range. However, the study of LiDAR data processing is usually divorced from the LiDAR sensor measurement process itself. This leads to critical measurement information being overlooked. This paper seeks a breakthrough to improve the performance of single-photon-avalanche-diode-based direct time-of-flight LiDAR systems by reviewing the data processing stages and corresponding processing approaches for LiDAR measurements, starting from photon incidence and ending with high-level feature recognition. Firstly, we propose a LiDAR system model based on data generation and transfer. The data forms in such a LiDAR system are mainly classified into timestamps, time-correlated histograms, point cloud data, and high-level properties. Subsequently, data processing methods applied to each of these data forms are analyzed. A number of hardware solutions closely related to data transmission and control are also included in the discussion. The principles, limitations, and challenges of these methods are discussed in detail and the criteria for evaluation of time-correlated histograms in ADAS are proposed. Finally, the research gaps in data processing are summarized, and future directions for research development are presented.

: http://publica.fraunhofer.de/dokumente/N-614368.html