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Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup.

Duarte Fernandes¹, Tiago Afonso², Pedro Girão²

¹Algoritmi Centre, University of Minho, 4800-058 Guimarães, Portugal.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

This summary is machine-generated.

This study introduces a low-cost, LiDAR-based vehicle platform for autonomous driving perception tasks. It demonstrates real-time Object Detection and SLAM capabilities on a resource-constrained system.

Keywords:

3D object detection LiDAR scanners SLAM autonomous driving deep learning methods onboard inference vehicles setup

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision for Autonomous Systems

Background:

Deep learning for autonomous driving heavily relies on LiDAR point clouds.
High sensor costs create barriers for low-cost autonomous driving solutions.
Critical perception tasks like Object Detection and SLAM require efficient processing.

Purpose of the Study:

To propose a low-cost, LiDAR-based test vehicle platform for autonomous driving.
To enable real-time performance of critical perception tasks on this platform.
To develop and deploy deep learning and SLAM models suitable for resource-constrained environments.

Main Methods:

Exploration of current vehicle platforms for sensor integration.
Development of a low-cost LiDAR-based test vehicle.
Implementation of a deep learning-based Object Detection model for resource-constrained devices.
Integration of a graph-based SLAM algorithm for real-time processing.

Main Results:

Demonstration of a functional low-cost autonomous driving test platform.
Successful real-time execution of Object Detection and SLAM algorithms.
Validation of the platform's usability for critical perception tasks.

Conclusions:

A low-cost, LiDAR-centric approach is feasible for real-time autonomous driving perception.
Resource-constrained deep learning and SLAM implementations can meet performance requirements.
The proposed platform lowers the barrier to entry for developing autonomous driving solutions.