Lidar Point clound processing for Autonomous Driving

A list of references on lidar point cloud processing for autonomous driving

Clustering/Segmentation (ground extraction, plane extraction)

• Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications [git]
• Time-series LIDAR Data Superimposition for Autonomous Driving [pdf]
• An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells
• Fast semantic segmentation of 3d point clounds with strongly varying density [pdf]
• A Fast Ground Segmentation Method for 3D Point Cloud [pdf]
• Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field [pdf]
• Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA [pdf]

Registration and Localization

• Point Clouds Registration with Probabilistic Data Association [git]
• Robust LIDAR Localization using Multiresolution Gaussian Mixture Maps for Autonomous Driving [pdf]

Feature Extraction

• Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR [pdf]
• Finding Planes in LiDAR Point Clouds for Real-Time Registration [pdf]
• Online detection of planes in 2D lidar [pdf]
• A Fast RANSAC–Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements [pdf]
• Hierarchical Plane Extraction (HPE): An Efficient Method For Extraction Of Planes From Large Pointcloud Datasets [pdf]
• A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing [pdf]

Object detection and Tracking

• Learning a Real-Time 3D Point Cloud Obstacle Discriminator via Bootstrapping pdf
• Terrain-Adaptive Obstacle Detection [pdf]
• 3D Object Detection from Roadside Data Using Laser Scanners [pdf]
• 3D Multiobject Tracking for Autonomous Driving : Masters thesis A S Abdul Rahman
• Motion-based Detection and Tracking in 3D LiDAR Scans [pdf]
• Lidar-histogram for fast road and obstacle detection [pdf]
• End-to-end Learning of Multi-sensor 3D Tracking by Detection pdf

Classification/Supervised Learning

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation link, link2
• SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud pdf
• Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes [pdf]
• DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet [pdf]

Map representations and Grids (HD Maps/ Occupancy grids/others)

• LIDAR-Data Accumulation Strategy To Generate High Definition Maps For Autonomous Vehicles [link]
• Detection and Tracking of Moving Objects Using 2.5D Motion Grids [pdf]
• 3D Lidar-based Static and Moving Obstacle Detection in Driving Environments: an approach based on voxels and multi-region ground planes [pdf]
• Spatio–Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments [pdf]
• Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling [pdf]

Lidar Datasets and Simulators

• Udacity based simulator link, git
• Tutorial on Gazebo to simulate raycasting from Velodyne lidar [link]
• Udacity Driving Dataset [link]
• Virtual KITTI [link]