Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation
Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped tw...
Main Authors: | Matthias Reuse, Karl Amende, Martin Simon, Bernhard Sick |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-02-01
|
Series: | Computer Sciences & Mathematics Forum |
Subjects: | |
Online Access: | https://www.mdpi.com/2813-0324/9/1/5 |
Similar Items
-
ZUST Campus: A Lightweight and Practical LiDAR SLAM Dataset for Autonomous Driving Scenarios
by: Yuhang He, et al.
Published: (2024-04-01) -
GAN-Based LiDAR Translation between Sunny and Adverse Weather for Autonomous Driving and Driving Simulation
by: Jinho Lee, et al.
Published: (2022-07-01) -
Adversarial robustness analysis of LiDAR-included models in autonomous driving
by: Bo Yang, et al.
Published: (2024-03-01) -
LiDAR Point Cloud Generation for SLAM Algorithm Evaluation
by: Łukasz Sobczak, et al.
Published: (2021-05-01) -
Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving
by: Xianjian Jin, et al.
Published: (2023-06-01)