Paper Info
Title
Data selection and de-noising based on reliability for long-range and high-pixel resolution LiDAR
Abstract
Although the Smart Accumulation Technique (SAT) [1] is an optimal averaging algorithm for realizing a long-range and high-pixel-resolution LiDAR system, its maximum effect is guaranteed by de-noising which is quite challenging due to the “range-value-clustering” problem peculiar to SAT. We propose a new algorithm that performs de-noising based on “reliability” provided by accumulating luminous intensities within a cluster. The simulation and measurement results show that the algorithm eliminates the clustering influence, and improves the maximum measurable range by about 2x on 99% of de-noised results, compared with the conventional approach. The overhead of the hardware implementation is small, 1% or less, in regard to Si area and power consumption.
Year
DOI
Venue
2018
10.1109/CoolChips.2018.8373079
2018 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)
Keywords
Field
DocType
LiDAR,de-noising,reliability,cluster,spatial relation,intensity information,background light information,averaging
Noise reduction,Data selection,Computer science,Measure (mathematics),Algorithm,Power demand,Lidar,Cluster analysis,Image resolution,Power consumption
Conference
ISSN
ISBN
Citations 
2473-4683
978-1-5386-6104-8
0
PageRank 
References 
Authors
0.34
2
4
Name
Order
Citations
PageRank
Ken Tanabe141.28
Hiroshi Kubota2112.00
Akihide Sai3208.25
Nobu Matsumoto400.68