Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian
and military fields, and have become the focus of research in many countries. Environmental …

Increasingly, the task of detecting and recognizing the actions of a human has been
delegated to some form of neural network processing camera or wearable sensor data. Due …

Detecting and tracking objects in 3D scenes play crucial roles in autonomous driving.
Successfully recognizing objects through space and time hinges on a strong detector and a …

Camera and LIDAR are both important sensor modalities for real-world applications,
especially autonomous driving. The sensors provide complementary information and make …

3D perception on point-cloud is a challenging and crucial computer vision task. A point-
cloud consists of a sparse, unstructured, and unordered set of points. To understand a point …

Multisensor fusion-based road segmentation plays an important role in the intelligent driving
system since it provides a drivable area. The existing mainstream fusion method is mainly to …

Place recognition is indispensable for a drift-free localization system. Due to the variations of
the environment, place recognition using single-modality has limitations. In this paper, we …

Recently, researchers have realized a number of achievements involving deep-learning-
based neural networks for the tasks of segmentation and detection based on 2D images, 3D …

Autonomous Vehicles (AVs) are expected to be intelligent enough to perceive the world
accurately in terms of avoiding road obstacles. Remarkable progress has been made in 3D …