Search

Your search keyword '"Roriz, Ricardo"' showing total 2 results
Start Over Author "Roriz, Ricardo" Topic perception system
2 results on '"Roriz, Ricardo"'

1. A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds.

Author

Roriz, Ricardo, Silva, Heitor, Dias, Francisco, and Gomes, Tiago

Subjects
*POINT cloud, *CLOUD storage, *DATA compression, *OPTICAL radar, *LIDAR, *GEOGRAPHICAL perception, *AUTOMOTIVE sensors
Abstract

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

2. A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors.

Author

Gomes, Tiago, Matias, Diogo, Campos, André, Cunha, Luís, and Roriz, Ricardo

Subjects
AUTOMOTIVE sensors, DRIVERLESS cars, OPTICAL radar, LIDAR, POINT cloud, AUTONOMOUS vehicles
Abstract

In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle's vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results