Learn how to use MATLAB to process lidar sensor data for ground, aerial and indoor lidar processing application. In this session you will learn how to use MATLAB to:

  • Import and visualize live and recorded lidar data
  • Apply deep learning to lidar data
  • Calibrate lidar and cameras
  • Track objects in lidar
  • Create 3-D maps and terrain maps using SLAM
  • Generate C/C++ and GPU Code

Highlights

  • Lidar Labeler App: Interactive, semi-automated, and custom automated labeling of lidar point clouds
  • Lidar-Camera Calibration: Calibrate lidar and camera sensors to estimate cross-sensor coordinate transform
  • Deep Learning for Lidar Point Cloud Processing: Use deep learning networks to detect and segment objects in lidar point cloud data
  • Shape Fitting: Fit shape and track detected objects in a lidar point cloud sequence
  • Feature Matching: Extract and match lidar point cloud features
  • Lidar Object Tracking
  • Simulating Lidar Sensor Data
  • 2-D Lidar Processing: Simulate and process 2-D laser scan data and estimate the pose between two scans
  • Velodyne LiDAR Streaming: Connect and stream lidar point clouds from Velodyne LiDAR sensors
  • Lidar File Readers: Support for Ibeosensor, LAS, and LAZ file formats
  • Code generation for CPU and GPU

About the Presenter:
Avinash Nehemiah, Principal Product Marketing Manager for computer vision, automated driving and deep learning at MathWorks, has ten years of experience in computer vision. Prior to joining MathWorks he led a team that created a computer vision-based solution for patient safety in hospital rooms. Avinash has a Master's degree in electrical and computer engineering from Carnegie Mellon University, where his research focused on object recognition in radar imagery.

Event Details

Please let us know if you require an accommodation in order to participate in this event. Accommodations may include live captioning, ASL interpreters, and/or captioned media and accessible documents from recorded events. At least 5 days in advance is recommended.

University of Pittsburgh Powered by the Localist Community Event Platform © All rights reserved