Lidar Point clound processing for Autonomous Driving

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

1

Clustering/Segmentation (ground extraction, plane extraction)

Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications [git:https://github.com/VincentCheungM/Run_based_segmentation]

Time-series LIDAR Data Superimposition for Autonomous Driving [pdf:http://lab.cntl.kyutech.ac.jp/~nishida/paper/2016/ThBT3.3.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:https://www.ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/timo-jan-isprs2016.pdf]

A Fast Ground Segmentation Method for 3D Point Cloud

Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field

[pdf:https://hal.inria.fr/hal-01579095/document]

Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA [pdf:https://arxiv.org/pdf/1711.02757.pdf]

2

Registration and Localization

Point Clouds Registration with Probabilistic Data Association [git:https://github.com/ethz-asl/robust_point_cloud_registration]

Robust LIDAR Localization using Multiresolution Gaussian Mixture Maps for Autonomous Driving

[pdf:https://www.ri.cmu.edu/pub_files/2008/9/peterson_kevin_2008_1.pdf]

3

Feature Extraction

Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR

[pdf:http://res.imtt.qq.com/m_download_qb/qbload_new_1.html]

Finding Planes in LiDAR Point Clouds for Real-Time Registration [pdf:http://ilab.usc.edu/publications/doc/Grant_etal13iros.pdf]

Online detection of planes in 2D lidar

A Fast RANSAC–Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements

Hierarchical Plane Extraction (HPE): An Efficient Method For Extraction Of Planes From Large Pointcloud Datasets

[pdf:http://vision.ucla.edu/papers/fontanelliRS07.pdf]

A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing

[pdf：https://hal-mines-paristech.archives-ouvertes.fr/hal-01097361/document]

4

Object detection and Tracking

Learning a Real-Time 3D Point Cloud Obstacle Discriminator via Bootstrapping [pdf:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.6290]

Terrain-Adaptive Obstacle Detection [pdf]

3D Object Detection from Roadside Data Using Laser Scanners [pdf:http://101.96.10.63/www-video.eecs.berkeley.edu/papers/JYT/spie-paper.pdf]

3D Multiobject Tracking for Autonomous Driving : Masters thesis A S Abdul Rahman

Motion-based Detection and Tracking in 3D LiDAR Scans [pdf:http://ais.informatik.uni-freiburg.de/publications/papers/dewan16icra.pdf]

Lidar-histogram for fast road and obstacle detection [pdf:http://www.chenliang.me/blog/wp-content/uploads/2017/07/lidarhistogram.pdf]

End-to-end Learning of Multi-sensor 3D Tracking by Detection [pdf:https://arxiv.org/pdf/1806.11534.pdf]

5

Classification/Supervised Learning

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

[pdf:https://arxiv.org/pdf/1710.07368.pdf]

Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes

[pdf:https://hal.archives-ouvertes.fr/hal-01182604/document]

DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet [pdf:http://home.isr.uc.pt/~cpremebida/files_cp/DepthCN_preprint.pdf]

6

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

LIDAR-Data Accumulation Strategy To Generate High Definition Maps For Autonomous Vehicles

[https://ieeexplore.ieee.org/document/8170357/]

Detection and Tracking of Moving Objects Using 2.5D Motion Grids

[pdf:http://a-asvadi.ir/wp-content/uploads/itsc15.pdf]

3D Lidar-based Static and Moving Obstacle Detection in Driving Environments: an approach based on voxels and multi-region ground planes [pdf:http://patternrecognition.cn/perception/negative2016a.pdf]

Spatio–Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments

[pdf:https://papers.nips.cc/paper/6541-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:https://arxiv.org/pdf/1705.08781.pdf]

7

Lidar Datasets and Simulators

Udacity based simulator [git:https://github.com/EvanWY/USelfDrivingSimulator]

Tutorial on Gazebo to simulate raycasting from Velodyne lidar [link:http://gazebosim.org/tutorials?tut=guided_i1]

Udacity Driving Dataset [link:https://github.com/udacity/self-driving-car/tree/master/datasets]

Virtual KITTI [link:http://www.europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds]

THE END