Perception of traversable regions and objects of interest from a 3D point cloud is one of the
critical tasks in autonomous navigation. A ground vehicle needs to look for traversable …

3D object detection and classification are crucial tasks for perception in Autonomous Driving
(AD). To promptly and correctly react to environment changes and avoid hazards, it is of …

The perception system has become a topic of great importance for autonomous vehicles, as
high accuracy and real-time performance can ensure safety in complex urban scenarios …

This article describes an approach for extracting multiple planar regions in 3-D point clouds
from spinning multibeam LiDARs. This technique benefits from the intrinsic structure of …

Advanced automotive perception systems have to meet high expectations in terms of cost-
effectiveness, performance, and robustness. The fusion of different sensor types …

To navigate safely, it is essential for a robot to detect all kinds of moving objects that could
possibly interfere with the own trajectory. For common object classes, like cars, regular …

This paper proposes fast and novel methods to jointly estimate the target's unknown 3D
shape and dynamics. Measurements are noisy and sparsely distributed 3D points from a …

LiDAR has become an indispensable sensor for autonomous vehicles due to its unique
properties. The clustering of non-ground point clouds, as an essential step of the perceptual …

In this paper, we present a simultaneous localization and mapping framework that combines
filter-based road course tracking and GraphSLAM for localization and mapping in …

Low-latency instance segmentation of LiDAR point clouds is crucial in real-world
applications because it serves as an initial and frequently-used building block in a robot's …