Sensor Fusion

1. List

2. Paper

• Performance Limits of Track-to-Track Fusion vs. Centralized Estimation: Theory and Application

• Track-to-track Fusion for Multi-target Tracking Using Asynchronous and Delayed Data: 2017, 69p

• A Survey of ADAS Technologies for the Future Perspective of Sensor Fusion

• VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach

• Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields: 2017.06

• Unstructured Environments: 농장
• lidar + camera sensing using a conditional random field
• Fusing LIDAR and images for pedestrian detection using convolutional neural networks: 2016.04

• Multiview random forest of local experts combining rgb and lidar data for pedestrian detection [Gonzalez, IV '15]

• Pedestrian Detection Combining RGB and Dense LIDAR Data [Premebida, IROS '14] [Project] [Code]

• Radar/Lidar sensor fusion for car-following on highways: 2011

• Shruti Gangadhar, SENSOR FUSION FRAMEWORK AND SIMULATION ON A TURTLEBOT ROBOTIC VEHICLE, 2017, 석사학위

• A comparative study of data fusion for RGB-D based visual recognition : A study of data fusion methods for RGB-D visual recognition can be found in Sanchez-Riera et al. [2016].

• C. Lundquist, “Sensor fusion for automotive applications,” Ph.D. dissertation, Linkoping University, Link ¨ oping, 2011. ¨

• N.-E. E. Faouzi, H. Leung, and A. Kurian, “Data fusion in intelligent transportation systems: Progress and challenges : A survey,” Information Fusion, vol. 12, no. 1, pp. 4 – 10, 2011.

• Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems: 2017, KITTI, 21p

• FUSION OF LIDAR 3D POINTS CLOUD WITH 2D DIGITAL CAMERA IMAGE: 2015, 석사학위, 90p, 라이다+카메라 -> RGB-D 정보 획득하기, transformatio 논문

• A Survey of ADAS Technologies for the Future Perspective of Sensor Fusion: 2016, 일반적 설명

• FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics: 2016

• FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture: 2016, Depth Information, Semantic Segmentation, Depth Image, CNN, RGB

• Multi-Sensor Fusion of Occupancy Grids based on Integer Arithmetic: Fusion 프레임워크 제안, 기본 익힌후 살펴 보기

• Fusing LIDAR and Images for Pedestrian Detection using Convolutional Neural Networks, Schlosser

• Motion-based Detection and Tracking in 3D LiDAR Scans, Dewan

• Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking: 2015, 관련연구의 퓨전 레벨 부분만 참고 함

• Object Perception for Intelligent Vehicle Applications:A Multi-Sensor Fusion Approach: 2014

• Sensor Modality Fusion with CNNs for UGV Autonomous Driving in Indoor Environments: 2017, 모형 자동차, 카메라+라이다 연동, DNN 제안

• Acoustic/Lidar Sensor Fusion for Car Tracking in City Traffic Scenarios

• Trends in Sensor and Data Fusion: 2005

3. Article (Post, blog, etc.)

• Expert Advice: Sensor Fusion for Highly Automated Driving: GNSS관련, 구체적 내용 없음

• Particle Filter Implementation: 하단

• 추천 : Tracking pedestrians for self driving cars

• 추천 : Tracking a self-driving car with high precision

• Object tracking with LIDAR, Radar, sensor fusion and Extended Kalman Filter: post

• Sensor Fusion Algorithms For Autonomous Driving

• Part 1 — The Kalman filter and Extended Kalman Filter

• Sensor Fusion with Kalman Filter (1/2)

• Sensor Fusion with Kalman Filter (2/2)

• Sensor Fusion

• Udacity carnd2 Sensor Fusion, Extended Karman Filter (English)

3. Tutorial (Series, )

4. Youtube

• Vehicle Detection using LiDAR and Camera sensor Fusion

• Why You Should Use The Kalman Filter Tutorial - Pokemon Example

6. Material (Pdf, ppt)

• Sensor Fusion and Calibration of Velodyne LiDAR and RGB Camera : ppt

• Sensor Fusion for Automotive Applications: 2011, 331p

7. Implementation (Project)

• An extended Kalman Filter implementation in Python for fusing lidar and radar sensor measurements: mithi, python
• Cpp버젼, CPP버젼v2

• Object-Tracking-and-State-Prediction-with-Unscented-and-Extended-Kalman-Filters : 변형

• Extended Kalman Filter Project Starter Code: Udacity

8. Research Group / Conference

Data fusion development with ROS | BASELABS: 필독, 샘플파일 제공