Publication Date: 10/17/2022

Event: 31st ACM International Conference on Information and Knowledge Management (CiKM 2022)

Reference: pp. 3371-3380, 2022

Authors: Md Parvez Mollah, The University of New Mexico; Biplob Debnath, NEC Laboratories America, Inc.; Murugan Sankaradas, NEC Laboratories America, Inc.; Srimat T. Chakradhar, NEC Laboratories America, Inc.; Abdullah Mueen, The University of New Mexico

Abstract: Roadside LiDAR (Light Detection and Ranging) sensors are recently being explored for intelligent transportation systems aiming at safer and faster traffic management and vehicular operations. A key challenge in such systems is to efficiently transfer massive point-cloud data from the roadside LiDAR devices to the edge connected through a 5G network for real-time processing. In this paper, we consider the problem of compressing roadside (i.e. static) LiDAR data in real-time that provides a unique condition unexplored by current methods. Existing point-cloud compression methods assume moving LiDARs (that are mounted on vehicles) and do not exploit spatial consistency across frames over time.To this end, we develop a novel grouped wavelet technique for static roadside LiDAR data compression (i.e. SLiC). Our method compresses LiDAR data both spatially and temporally using a kd-tree data structure based on Haar wavelet coefficients. Experimental results show that SLiC can compress up to 1.9× more effectively than the state-of-the-art compression method can do. Moreover, SLiC is computationally more efficient to achieve 2× improvement in bandwidth usage over the best alternative. Even with this impressive gain in communication and storage efficiency, SLiC retains down-the-pipeline application’s accuracy.

Publication Link: https://dl.acm.org/doi/10.1145/3511808.3557144