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
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.
Subject(s)
Automobile Driving , Automobiles , Autonomous Vehicles , Spectrum Analysis, RamanABSTRACT
BACKGROUND: Coronary vasculopathy is the main cause of cardiac graft failure. Because yearly coronary angiography is invasive in children, a non-invasive method for detecting graft vasculopathy is needed. The aim of this study was to test dobutamine-stress echocardiography in a pediatric population to determine its feasibility, safety and reliability in the detection of graft coronary artery disease. METHODS: Eighteen patients, aged 2 days to 16.8 years at transplantation (mean 8.4 years), underwent 44 dobutamine-stress echocardiography (DSE) exams, at a follow-up of 1.1 to 11.8 years (mean 5.1 years). Selective coronary angiography was performed for comparison. Echocardiographic recordings were obtained in 4 standard views of the left ventricle and measurements carried out within the frames of a 16-segment model. Segmental scores of contractility were obtained for each segment and a total segmental contractility index was calculated at each stage. RESULTS: All patients reached the maximum dose stage. Maximum heart rate was 57% to 90% of predicted maximum. Maximum systolic blood pressure reached 190 mmHg. Segmental scores were normal in 37 and abnormal in 7 cases. Echographic results were concordant with angiography in 82% and discordant in 18% of the cases (4 negative DSEs with minor angiographic lesions, 2 positive DSEs with normal angiography), but there was no significant angiographic lesion with normal DSE. CONCLUSIONS: DSE is a safe and highly feasible non-invasive technique in transplanted children. A normal DSE study successfully predicts the absence of significant coronary artery disease in the post-transplant population.
