Cargando…
Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving Applications
Research about deep learning applied in object detection tasks in LiDAR data has been massively widespread in recent years, achieving notable developments, namely in improving precision and inference speed performances. These improvements have been facilitated by powerful GPU servers, taking advanta...
Autores principales: | Silva, António, Fernandes, Duarte, Névoa, Rafael, Monteiro, João, Novais, Paulo, Girão, Pedro, Afonso, Tiago, Melo-Pinto, Pedro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659874/ https://www.ncbi.nlm.nih.gov/pubmed/34883937 http://dx.doi.org/10.3390/s21237933 |
Ejemplares similares
-
Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup
por: Fernandes, Duarte, et al.
Publicado: (2021) -
A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications
por: Silva, António Linhares, et al.
Publicado: (2023) -
Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data
por: Silva, João, et al.
Publicado: (2022) -
The preference of onboard activities in a new age of automated driving
por: Hamadneh, Jamil, et al.
Publicado: (2022) -
Onboard Computers, Onboard Software and Satellite Operations: An Introduction
por: Eickhoff, Jens
Publicado: (2012)