LiDAR Perception for Autonomous Driving | | Software Development

LiDAR Perception for Autonomous Driving
This training covers classical point cloud processing methods for ADAS as well as deep learning based methods for Autonomous Driving.
8 hours
Online
English
AUT-028
LiDAR Perception for Autonomous Driving
Sign Up
Duration
8 hours
Location
Online
Language
English
Code
AUT-028
Schedule and prices
€ 400
Training for 7-8 or more people? Customize trainings for your specific needs
LiDAR Perception for Autonomous Driving
Sign Up
Duration
8 hours
Location
Online
Language
English
Code
AUT-028
Schedule and prices
€ 400
Training for 7-8 or more people? Customize trainings for your specific needs

Description

With the introduction of LiDAR sensors (Light Detection and Ranging) to ADAS (Advanced Driver Assistance Systems) and Autonomous Driving, a need for perception algorithms that directly analyze point clouds has become apparent.

In this training you’ll:
  • get an overview of ADAS and Autonomous Driving from the perspective of processing LiDAR data
  • understand the traditional system setup and coordinate frames, notions of latency and jitter
  • understand the details of classical point cloud processing algorithms for ADAS scenario
  • get hands-on experience with implementing at least one of the classical algorithms in C++
  • get an overview of deep learning approaches to perception in the autonomous driving scenario
  • understand how to measure accuracy of the algorithms and deploy them to state of the art hardware
  • get an overview of open datasets for autonomous driving
After completing the course, a certificate
is issued on the Luxoft Training form

Objectives

  • After this training you’ll be able to understand a spectrum of classical and deep learning perception algorithms that process point cloud data from a LiDAR.
  • Get hands-on experience in implementing a selected algorithm in C++

Target Audience

  • This course is designed for computer vision algorithm developers in the automotive field

Roadmap

a:2:{s:4:"TEXT";s:2586:"Brief introduction to ADAS and Autonomous Driving
  • Levels of autonomy, classic AD stack
  • Players on the market, LiDAR mount options
  • LiDAR technological directions
  • Overview of LiDAR vendors and models
  • Characteristics of Velodyne’s LiDARs
  • ASIL levels, ISO26262

Basic system setup
  • Coordinate systems (global, local, ego-vehicle, sensor, other traffic participants’)
  • Calibration
  • Synchronization
  • Latency and jitter

Classical point-cloud perception algorithms
  • Overview of perception tasks solvable with LiDAR
  • Multi-frame accumulation (motion compensation)
  • Ground detection/subtraction
  • Occupancy grid
  • Clusterization (DBscan)
  • Convex hull estimation
  • Lane detection from a point cloud

Practical exercise
  • Review of the code that implements ground plane removal, clusterization, convex hull extraction and visualization in C++ with Eigen and PCL libraries.
  • Practical task to implement one of the following algorithms: Ground plane removal with RANSAC & Convex hull calculation with Graham scan

Perception with neural networks
  • Introduction into deep learning based approaches
  • Taxonomy of neural networks for point cloud processing
  • Basic block: PointNet
  • VoxelNet (BEV detection)
  • SECOND (BEV detection)
  • PointPillars (BEV detection)
  • Fast and Furious (BEV detection and prediction)
  • Frustum PointNet (projection view, detection)
  • MV3D (mutiview detection)
  • Multiview fusion, MVF (mutiview detection)
  • Multi-View LidarNet (multitarget: segmentation and detection)

Open datasets for autonomous driving
  • KITTI
  • Semantic KITTI
  • nuScenes
  • Waymo
  • Argoverse
  • Lyft Level-5
  • Udacity

Continuous deployment of deep learning models
  • Accuracy metrics
  • Non-regressive deployment

Compute platforms for autonomous driving
  • Overview of platforms: DrivePX2, Pegasus, Mobileye, Tesla’s board computer
  • TensorRT inference library
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