@inproceedings{98ff4271fb1944918314e3ac61ec8533,
title = "Indirect Time of Flight Near Field LiDAR Depth Correction Using Spiking Neural Networks",
abstract = "Spiking Neural Networks (SNN) is a machine learning model inspired by the spiking nature of biological human brain neurons. These neural models lead to the creation of neuromorphic computing chips, which can execute at a very low power profile, less than 1 Watt. Such a low profile can be very useful in developing low-power sensors. In this paper, we investigate the feasibility of implementing our iToF LiDAR depth correction model for static scenes using SNN, leveraging the advantages of neuromorphic chips to develop low-power sensors suitable for electric vehicles and battery-powered autonomous mobile robots. We present our results, findings, and recommendations to implement such a system. We also discuss the challenges we have faced and the possible solutions to overcome them. The paper has benchmarked the new SNN-based model versus the original ANN model in terms of accuracy, and it has been found that SNN-based models can provide comparable accuracy under certain conditions.",
keywords = "depth correction, LiDAR, lowpower, neural networks, neuromorphic, spiking",
author = "Mena Nagiub and Thorsten Beuth and Ganesh Sistu and Heinrich Gotzig and Ciaran Eising",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 5th International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2025 ; Conference date: 17-09-2025 Through 18-09-2025",
year = "2025",
doi = "10.1109/MIUCC66482.2025.11196827",
language = "English",
series = "5th International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "450--457",
editor = "Bahaa-Eldin, \{Ayman M.\} and Ashraf AbdelRaouf and Nada Shorim and Nada Nofal and Yasmine Kandil",
booktitle = "5th International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2025",
}