5 accepted papers
Robust feature matching forms the backbone for most Visual Simultaneous Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and Structure from Motion (SfM) algorithms. However, recovering feature matches from texture-poor scenes is a major challenge and still remains an open area o…
Light Detection And Rangings (LiDARs) have been widely adopted to modern self-driving vehicles, providing 3D information of the scene and surrounding objects. However, adverser weather conditions still pose significant challenges to LiDARs since point clouds captured during snowfall can easily be co…
Risk assessment to quantify the danger associated with taking a certain action is critical to navigating safely through crowded urban environments during autonomous driving. Risk assessment and subsequent planning is usually done by first tracking and predicting trajectories of other agents, such as…
Navigating safely in urban environments remains a challenging problem for autonomous vehicles. Occlusion and limited sensor range can pose significant challenges to safely navigate among pedestrians and other vehicles in the environment. Enabling vehicles to quantify the risk posed by unseen regions
This letter presents a novel dataset titled PedX, a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5 000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing two-dimensional (2-D) image labels and 3-D labels of