LiDAR-Based Occupancy Grid Map Estimation Exploiting Spatial Sparsity

Conference paper (2023)

Authors

Çağan Önen NXP Semiconductors
Ashish Pandharipande NXP Semiconductors
G. Joseph Signal Processing Systems - EEMCS
N.J. Myers Team Nitin Myers - Mechanical, Maritime and Materials Engineering
Research Group
Team Nitin Myers (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Nitin Myers

Abstract

The problem of estimating occupancy grids to support automotive driving applications using LiDAR sensor point clouds is considered. We formulate the problem as a sparse binary occupancy value reconstruction problem. Our proposed occupancy grid estimation method is based on pattern-coupled sparse Bayesian learning and exploits the inherent sparsity and spatial occupancy dependencies in LiDAR sensor measurements. The proposed method demonstrates enhanced detection capabilities compared to commonly used benchmark methods, as observed through testing on scenes from the nuScenes dataset.

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LiDAR-Based_Occupancy_Grid_Map... (pdf)
(pdf | 0.775 Mb)
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