Codes and Research paper links for Lidar and Camera

Codes links for implementation on LiDAR and Camera

• https://modelnet.cs.princeton.edu/

LiDAR

1. Codes for "Learning Lightweight Lane Detection CNNs by Self Attention Distillation"

   • https://github.com/cardwing/Codes-for-Lane-Detection

2. Spatial CNN for Traffic Lane Detection

   • https://github.com/XingangPan/SCNN
   • 

3. See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation

   • https://arxiv.org/pdf/2111.09450.pdf
   • Code : https://github.com/darrenjkt/SEE-MTDA
   • 

4. Simultaneous Location of Rail Vehicles and Mapping of Environment with Multiple LiDARs

   • https://arxiv.org/pdf/2112.13224.pdf

5. Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)

   • https://github.com/cardwing/Codes-for-PVKD

6. V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer (ECCV 2022)

   • https://github.com/DerrickXuNu/v2x-vit

LiDAR-Inertial Odometry

Father of LIDAR and IMU integration

• https://www.researchgate.net/profile/Fu-Zhang-14

1. LIO-Livox (A Robust LiDAR-Inertial Odometry for Livox LiDAR)

   • Code: https://github.com/Livox-SDK/LIO-Livox

2. LIO-mapping: A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach -- ICRA 2019

   • Code: https://github.com/hyye/lio-mapping

3. LOAM_NOTED

   • Code: https://github.com/cuitaixiang/LOAM_NOTED

4. FAST-LIO Robust Real-time LiDAR-inertial Initialization

   • Code: https://github.com/hku-mars/FAST_LIO
   • Fast LiDAR-Inertial Odometry is a computationally efficient and robust LiDAR-inertial odometry package.
     • It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs.

5. FAST-LIO2: Fast Direct LiDAR-inertial Odometry

   • https://arxiv.org/pdf/2107.06829.pdf
   • Code : https://github.com/hku-mars/FAST_LIO (2021-07-05 Update)

Camera

1. zed-examples

   • Code: https://github.com/Amit10311/zed-examples

2. Mask R-CNN for Object Detection and Segmentation

   • Clone reposotory git clone https://github.com/matterport/Mask_RCNN.git
   • Code : https://github.com/NNtamp/Personal-Repository
   • 

3. Segment Anything

   • https://ai.facebook.com/research/publications/segment-anything/
   • Code : https://github.com/facebookresearch/segment-anything
   • 

Fusion model (LiDAR + Cameras)

1. A Deep Learning Approach for LiDAR Resolution-Agnostic Object Detection

   • https://hal.archives-ouvertes.fr/hal-03485613/document (2021)
   • 

2. Camera LiDAR Calibration ROS Package Tutorial

   • The full paper is accessible at: https://arxiv.org/abs/2103.12287
   • The code is available at: https://github.com/acfr/cam_lidar_calibration

3. Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion

   • The full paper is accessible at: https://arxiv.org/abs/2203.09780
   • The code is available at: https://github.com/LittlePey/SFD
   • 

Point Cloud

1. Voxel-Based Methods

• VFF: Voxel Field Fusion for 3D Object Detection

  • VFF aims to maintain cross-modality consistency by representing and Image fusing augmented image features as a ray in the voxel field.
  • Code: https://github.com/dvlab-research/VFF
  • 

• PIXOR : Real-time 3D Object Detection from Point Clouds

  • https://arxiv.org/pdf/1902.06326.pdf
  • Code : https://github.com/philip-huang/PIXOR

• PointPillars : Fast Encoders for Object Detection from Point Clouds

  • https://arxiv.org/pdf/1812.05784.pdf
  • Code : https://github.com/nutonomy/second.pytorch
  • https://github.com/NVIDIA-AI-IOT/CUDA-PointPillars

2. Point-base Methods : Suitable for in-door scenes (high object density) not for out-door ( large scale point clouds)

• PointNet++ : Deep Hierarchical Feature Learning on Point Sets in a Metric Space - 2017

  • https://arxiv.org/pdf/1706.02413.pdf
  • Code : https://github.com/charlesq34/pointnet2

• PointRCNN : 3D Object Proposal Generation and Detection from raw Point Cloud

  • https://arxiv.org/pdf/1812.04244.pdf
  • The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results.
  • Code : https://github.com/sshaoshuai/PointRCNN

3. 3D Machine Learning 201 Guide: Point Cloud Semantic Segmentation

   • https://lnkd.in/dv88uk39

4. General-Purpose Point Cloud Feature Extractor

   • Code: https://github.com/WDot/G3DNet

5. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

   • Code: https://github.com/qianguih/voxelnet

6. OpenPCDet

   • OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection.
   • Code: https://github.com/open-mmlab/OpenPCDet

DEEP-LEARNING

1. FUNDAMENTALS OF DEEP LEARNING

   • https://github.com/Amit10311/FUNDAMENTALS-OF-DEEP-LEARNING

2. FUNDAMENTALS OF COMPUTING WITH CUDA C/C++

   • https://github.com/Amit10311/FUNDAMENTALS-OF-COMPUTING-WITH-CUDA-C-C-

3. Deep-Learning-for-Robotics

   • https://github.com/Amit10311/Deep-Learning-for-Robotics

4. NVIDIA Deep Learning Examples for Tensor Cores

   • https://github.com/NVIDIA/DeepLearningExamples

5. PyTorch Tutorial to Object Detection.

   • https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection

Robots

1. Ros_Summit_XL_basics

   • Code: https://github.com/Amit10311/Ros_Summit_XL_basics

2. Summit XL Navigation

• Code: https://github.com/Mostafa-wael/Summit-XL-Navigation

Simulators

1. TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.

   • https://github.com/ika-rwth-aachen/Cam2BEV

2. Open-RMF (www.open-rmf.org)

   • Open-RMF ROSCon Workshop slides https://lnkd.in/gvnaeXnx
   • https://github.com/open-rmf/rmf-web
   • https://github.com/open-rmf/rmf_demos
   • https://vimeo.com/405803151

3. Robust and efficient coverage paths for autonomous agricultural vehicles

   • https://github.com/Fields2Cover/Fields2Cover

Working with Drone

1. Intelligent Quads Tutorials

   • https://github.com/Intelligent-Quads/iq_tutorials

2. mav_trajectory_generation

   • Tools for polynomial trajectory generation and optimization.
   • https://github.com/ethz-asl/mav_trajectory_generation

Programming

1. Classic Programming Books
   • https://github.com/jobbole/awesome-programming-books

Courses

1. Sensor Fusion Udacity

   • https://github.com/YoungGer/Udacity-SensorFusion-NanoDegree
   • https://github.com/fanweng/Udacity-Sensor-Fusion-Nanodegree/projects

2. Self-Driving-Car-Engineer-Nanodegree

   • https://github.com/ChenBohan/Udacity-Self-Driving-Car-Engineer-Nanodegree
   • https://github.com/ChenBohan/Robotics-Control-01-PID-Control
   • https://github.com/ChenBohan/Robotics-Sensor-Fusion-03-UKF-Unscented-Kalman-Filter
   • https://github.com/ChenBohan/Robotics-Path-Planning-05-CarND-Path-Planning-Project

3. Robotics-ROS

   • https://github.com/ChenBohan/Robotics-ROS-04-Coordinate-Transform

GITHUB

1. Git CI Actions
   • https://github.com/actions/cache