Interesting literature (and other resources) on autonomous systems

If you published something on these topics, then drop me a note and I will see if it qualifies to get listed here.

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Contents

• Artificial Intelligence
• Computer Vision
• Datasets
• Deep Learning, Machine Learning and Optimization
• GPS
• LiDAR
• RADAR
• Self-driving cars
• Simulators
• SLAM, State Estimation and Pose Estimation
• UAVs (Unmanned Aerial Vehicles aka Drones)
• Misc

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Artificial Intelligence

• Russell and Norvig (2009): Artificial Intelligence: A Modern Approach. http://aima.cs.berkeley.edu/

Computer Vision

• Acuna et al. (2019): Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations. arXiv:1904.07934

• Berg and Haddad (2016): Visual Odometry for Road Vehicles Using a Monocular Camera. A comparison of Feature Matching and Feature Tracking using FAST, SURF, and SIFT detectors. Master Thesis, Gothenburg. PDF

• Corke (2011): Robotics, Vision and Control. link

• Forsyth and Ponce (2012): Computer Vision: A Modern Approach, 2nd Edition.

• Fu et al. (2019): Deep Ordinal Regression Network for Monocular Depth Estimation. arXiv:1806.02446

• Hartley and Zisserman (2003). Multiple view geometry in computer vision. Cambridge university press.

• Nunez et al. (2011): Visual Odometry Based on Structural Matching of Local Invariant Features Using Stereo Camera Sensor. Sensors 2011, 11(7), 7262-7284. DOI:10.3390/s110707262

• Pfeuffer and Dietmayer (2019): Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion. arXiv:1905.10117

• Pillai et al. (2018): SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation. arXiv:1810.01849

• Szeliski (2010): Computer Vision: Algorithms and Applications. http://szeliski.org/Book/

• Wofk et al. (2019): FastDepth: Fast Monocular Depth Estimation on Embedded Systems. arXiv:1903.03273

Datasets

Have a look at a different list, please

Deep Learning, Machine Learning and Optimization

• Bengio (2012): Practical Recommendations for Gradient-Based Training of Deep Architectures. arXiv:1206.5533

• Gaudet and Maida (2017): Deep Quaternion Networks. arXiv:1712.04604
• Goodfellow et al. (2016): Deep Learning. https://www.deeplearningbook.org/

• Greenblatt and Agaian (2018): Introducing quaternion multi-valued neural networks with numerical examples. Information Sciences 423, 326-342. DOI:10.1016/j.ins.2017.09.057

• Kosiorek et al. (2019): Stacked Capsule Autoencoders. arXiv:1906.06818

• OpenAI (): Spinning up Deep RL. https://spinningup.openai.com/en/latest/index.html

• Parcollet et al. (2018): Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition. arXiv:1806.07789
• Parcollet et al. (2018): Quaternion Recurrent Neural Networks. arXiv:1806.04418

• Parcollet et al. (2018): Quaternion Convolutional Neural Networks for Heterogeneous Image Processing. arXiv:1811.02656

• Sabour et al. (2017): Dynamic Routing Between Capsules. arXiv:1710.09829

• Sutton and Barto (2018): Reinfocement Learning: An Introduction. http://incompleteideas.net/book/the-book-2nd.html

• Zhu et al. (2019): Quaternion Convolutional Neural Networks. arXiv:1903.00658

GPS

• Comma.ai Laika

IMUs

LiDAR

• Ali et al. (2018): YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud. arXiv:1808.02350

• Biasutti et al. (2019): RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud. arXiv:1905.08748

• Biasutti et al. (2019): LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net. arXiv:1908.11656

• Dewan and Burgard (2019): DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans. arXiv:1906.06962

• Heinzler et al. (2019): Weather Influence and Classification with Automotive Lidar Sensors. In: IEEE IV 2019 Proceedings. doi:10.1109/IVS.2019.8814205

• Wang et al. (2019): PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud. arXiv:1807.06288

Planning and prediction

• de Brebisson et al. (2015): Artificial Neural Networks Applied to Taxi Destination Prediction. arXiv:1508.00021

• Huegle et al. (2019): Dynamic Input for Deep Reinforcement Learning in Autonomous Driving. arXiv:1907.10994

• Karaman and Frazzoli (2013): Sampling-based Optimal Motion Planning for Non-holonomic Dynamical Systems. Proceedings - IEEE International Conference on Robotics and Automation. DOI: 10.1109/ICRA.2013.6631297

• LaValle (2006): Planning Algorithms. Cambridge University Press, 842 pages. pdf edition

• Pivtoraiko et al. (2009): Differentially Constrained Mobile Robot Motion Planning in State Lattices. Journal of Field Robotics 26(3), 308-333. DOI:10.1002/rob.20285

• Tamar et al. (2016): Value Iteration Networks. arXiv:1602.02867

RADAR

• Brodeski et al. (2019): Deep Radar Detector. arXiv:1906.12187

• Fenn (2010): Adaptive Antennas and Phased Arrays. https://www.ll.mit.edu/outreach/adaptive-antennas-and-phased-arrays

• Gardill (2019): Automotive Radar - An Overview on State-of-the-Art Technology. IEEE MTT-S/YouTube:P-C6_4ceY64

• O’Donnel (2009): Introduction to Radar Systems. https://www.ll.mit.edu/outreach/introduction-radar-systems

• Rock et al. (2019): Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks. arXiv:1906.10044

• Roy et al (2019): One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification. In BuildSys ’19: ACM Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November 13–14, 2019, New York, NY . ACM, New York, NY, USA, 10 pages. arXiv:1909.03082

• Schöller et al. (2019): Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems. arXiv:1904.08743

• Wolff (ongoing): Radar Basics. radartutorial.eu

Robotics

• Francis and Maggiore (2016): Flocking and Rendezvous in Distributed Robotics. In: SpringerBriefs in Control, Automation and Robotics. Springer International Publishing, 105 pages. DOI:10.1007/978-3-319-24729-8

• Thrun et al. (2005): Probabilistic Robotics. http://probabilistic-robotics.org/

Self-driving cars

• Bussemaker (2014): Sensing requirements for an automated vehicle for highway and rural environments. Master Thesis, Delft. https://repository.tudelft.nl/islandora/object/uuid%3A2ae44ea2-e5e9-455c-8481-8284f8494e4e

• Jacobsen et al. (2016): Vehicle Dynamics Compendium. https://pingpong.chalmers.se/public/courseId/7244/lang-en/publicPage.do

• Rajamani (2012): Vehicle Dynamics and Control. Springer US, 498 pages. DOI:10.1007/978-1-4614-1433-9

• Reinholtz et al. (2007): DARPA Urban Challenge Technical Paper. Odin: Team VictorTango. https://archive.darpa.mil/grandchallenge/TechPapers/Victor_Tango.pdf

• Urmson et al. (2008): Autonomous Driving in Urban Environments: Boss and the Urban Challenge. Journal of Field Robotics 25(8), 425 - 466. DOI:10.1002/rob.20255

Simulators

• Dosovitskiy et al. (2017): CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Annual Conference on Robot Learning, 1-16. PDF talk

SLAM, State Estimation and Pose Estimation

• Barfoot (2017). State Estimation for Robotics. Cambridge University Press, 394 pages. http://asrl.utias.utoronto.ca/~tdb/

• Bender et al. (2014): Lanelets: Efficient Map Representation for Autonomous Driving. IEEE Intelligent Vehicles Symposium (IV)June 8-11, 2014. Dearborn, Michigan, USA. 420 - 425. DOI: 10.1109/IVS.2014.6856487

• Caruso et al. (2015): Large-scale Direct SLAM for Omnidirectional Cameras. URL: https://vision.in.tum.de/_media/spezial/bib/caruso2015_omni_lsdslam.pdf

• Farrell and Roysdon (2017): Advanced Vehicle State Estimation: A Tutorial and Comparative Study. IFAC-PapersOnLine 50(1), 15971-15976. DOI:10.1016/j.ifacol.2017.08.1751

• Julier and Uhlmann (1997): New extension of the Kalman filter to nonlinear systems. Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997). DOI:10.1117/12.280797

• Ludwig and Burnham (2018): Comparison of Euler Estimate using Extended Kalman Filter, Madgwick and Mahony on Quadcopter Flight Data. International Conference on Unmanned Aircraft Systems (ICUAS’18), Dallas, Texas, USA, June 2018. pdf

• Mur-Artal and Tardós (2017): ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics, vol. 33 (5), 1255-1262, 2017. PDF

• Sola (2017): Quaternion kinematics for the error-state Kalman filter. arXiv:1711.02508

• Terejam (2009): Unscented Kalman Filter Tutorial. https://cse.sc.edu/~terejanu/files/tutorialUKF.pdf

• Wang et al. (2017): Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras. URL: https://vision.in.tum.de/_media/spezial/bib/wang2017stereodso.pdf

• Welch (ongoing): The Kalman Filter. A large collection of everything about various flavors of Kalman filter. https://www.cs.unc.edu/~welch/kalman/

• Ye and Liu (2017): LiDAR and Inertial Fusion for Pose Estimation by Non-linear Optimization. ICRA 2018. arXiv:1710.07104

UAVs (Unmanned Aerial Vehicles aka Drones)

• Kumar (????): Aerial Robotics. https://www.youtube.com/playlist?list=PLblGgzWkqSqM7IWsgjDetdzZDS1NbkTnd. Videos from the Coursera Robotics Specialization