GeoComp demos

Lidar based object tracking from a moving vehicle for Velodyne HDL64 sensor data

Crossmodal Point Cloud Registration for Mobile Laser Scanning Data tested with Velodyne HDL64, VLP16 and Riegl VMX450 sensors

Published @ International Conference on Pattern Recognition 2016

GPS/IMU free SLAM for Velodyne HDL64 and VLP16 sensors

Published @ International Conference on Pattern Recognition 2016

A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds

Published @ Scene Understanding for Autonomous Systems at ACCV'14

Fast 3-D Urban Object Detection on Streaming Point Cloud: we present a simple, yet efficient hierarchical grid data structure and corresponding algorithms that significantly improve the processing speed of the object detection task.

Published @ Road Scene Understanding and Autonomous Driving at ECCV'14

Real time point cloud segmentation for Velodyne HDL64 and VLP16 sensors: are able to automatically interpret the LIDAR point cloud stream obtained from a moving platform, segment different point cloud classes,

Published @ ISPRS Workshop on 3D Virtual City Modeling, 2013

Zebra crossing and street object detection we perform real-time localization and identification of typical urban objects, such as traffic signs, vehicles or crosswalks.

Published @ CogInfoCom 2013,

Point cloud filtering: vegetation detection, point cloud enhancement, and moving-static object separation

Published @ IEEE Int'l Workshop on Content-Based Multimedia Indexing, 2013

Data aquisition point cloud sequences are obtained from a moving vehicle, using a Velodyne HDL-64E Rotating Multi-Beam LIDAR sensor.