My gratitude to Sir Sebastion Thrun, Sir Peter Norvig, Sir Thad Starner,Sir Nicholas Roy, Sir David J. Malan, Sir David Silver, Sir Raffaello D'Andrea, Sir Ryan, Madam Dana Sheahen, Instructor Alexis Cook, Instructor Erica, Instructor Karim, Instructor Julia, Sir Stephen, Sir Aaron Brown, Sir Andy, Instructor Stefanie, Instructor Angela, Sir Jonathan, Instructor Liz, Instructor Eddie, Instructor Miriam, Instructor Cindy, Instructor Brok, Instructor Arpan, Sir Jake, Sir Michael Virgo, Sir Tucker and Sir Akshit.
To memorize the journey start from self-driving Car Engineer Nanodegree Program to Artificial Intelligence Nanodegree Program to Flying Car Nanodegree Program then Robotics Software Nanodegree Program and CS50: Introduction to Computer Science 2018, To memorize the year of my enrollment in Udacity from 2017 to present.
To memorize mentors Donald, Christopher, Jafar and David who encourage and support me a lot through my enrollment years. Here I collect the feedback and paper references from my Udacity reviewers and friendships I got from Udacity and CS50X. I want to extend what I learn and also extend the spirits, share knowledge with all my classmates at least this is what I can do if any of this citation papers helps you please send a star to them or references their papers will be an honorable action:).
Within these periods I want to say thank you to Madam Olga Uskova, Mylene doublet o'kane and Sir Luigi Morelli. I love your writings and very appreciate every like you send to me. My gratitude to Martin McGovern, Karen E. Baker, and Udacity team you let me learn to be Udacious and persistence:D!
https://en.wikipedia.org/wiki/Olga_Uskova
https://vimeo.com/cognitivetech
http://www.taipeitimes.com/News/feat/archives/2018/01/08/2003685375
http://www.agriland.ie/farming-news/fully-driverless-combine-harvester-by-2024/
https://airccj.org/CSCP/vol5/csit53211.pdf
https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
http://www.kerrywong.com/2009/05/07/canny-edge-detection-auto-thresholding/
https://medium.com/@vivek.yadav/improved-performance-of-deep-learning-neural-network-models-on-traffic-sign-classification-using-6355346da2dc
https://medium.com/@jeremyeshannon/udacity-self-driving-car-nanodegree-project-2-traffic-sign-classifier-f52d33d4be9f
https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py
https://keras.io/callbacks/#callback
https://keras.io/callbacks/#modelcheckpoint
http://cs229.stanford.edu/proj2015/054_report.pdf
http://ruder.io/optimizing-gradient-descent/
http://ruder.io/optimizing-gradient-descent/index.html#adam
http://alexlenail.me/NN-SVG/LeNet.html
https://keras.io/visualization/
https://www.researchgate.net/publication/257291768_A_Much_Advanced_and_Efficient_Lane_Detection_Algorithm_for_Intelligent_Highway_Safety
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017478/
https://medium.com/@mohankarthik/feature-extraction-for-vehicle-detection-using-hog-d99354a84d10
https://chatbotslife.com/towards-a-real-time-vehicle-detection-ssd-multibox-approach-2519af2751c
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
https://stackoverflow.com/questions/27466642/what-kind-of-optimization-does-const-offer-in-c-c
https://www.youtube.com/watch?v=aUkBa1zMKv4
http://correll.cs.colorado.edu/?p=965
https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume9/mazer98a-html/node2.html
http://www.roborealm.com/help/Path_Planning.php
https://robotics.stackexchange.com/questions/8302/what-is-the-difference-between-path-planning-and-motion-planning
https://www.robotshop.com/community/forum/t/excellent-tutorial-on-a-robot-path-planning/13170
https://www.mathworks.com/help/robotics/examples/path-planning-in-environments-of-difference-complexity.html;jsessionid=a7ff890a4e697fe79be723535659
http://ais.informatik.uni-freiburg.de/teaching/ss11/robotics/slides/18-robot-motion-planning.pdf
http://ais.informatik.uni-freiburg.de/teaching/ss10/robotics/slides/16-pathplanning.pdf
http://ai.stanford.edu/~ddolgov/papers/dolgov_gpp_stair08.pdf
https://webpages.uncc.edu/~jmconrad/GradStudents/Thesis_Ghangrekar.pdf
http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review
https://arxiv.org/pdf/1411.4038.pdf
http://cs231n.github.io/neural-networks-2/#init
http://cs231n.github.io/neural-networks-2/#reg
https://stats.stackexchange.com/questions/164876/tradeoff-batch-size-vs-number-of-iterations-to-train-a-neural-network
https://www.jeremyjordan.me/nn-learning-rate/
https://arxiv.org/abs/1806.02446
https://arxiv.org/abs/1903.03273
https://arxiv.org/abs/1901.00114
https://roscon.ros.org/2018/#program
http://driving.stanford.edu/papers/ISER2010.pdf
http://planning.cs.uiuc.edu/
https://docs.ufpr.br/~danielsantos/ProbabilisticRobotics.pdf
https://www.youtube.com/playlist?list=PLX2gX-ftPVXU3oUFNATxGXY90AULiqnWT
https://simondlevy.academic.wlu.edu/
https://docs.google.com/viewer?url=https%3A%2F%2Fwww.seas.harvard.edu%2Fcourses%2Fcs281%2Fpapers%2Funscented.pdf
http://andrew.gibiansky.com/downloads/pdf/Quadcopter%20Dynamics,%20Simulation,%20and%20Control.pdf
http://aeroconf.org/
https://arxiv.org/abs/1902.01465
http://navion.mit.edu/
https://www.researchgate.net/publication/322020415_Adaptive_Super-twisting_Second-order_Sliding_Mode_Control_for_Attitude_Control_of_Quadcopter_UAVs
https://arxiv.org/abs/1801.10130
https://utm.arc.nasa.gov/documents.shtml
https://people.csail.mit.edu/rivest/pubs/Riv87c.pdf
https://www.semanticscholar.org/paper/Deep-Blue-Campbell-Hoane/378f933bbdb70d6f373e32e7182b6a5669c95d02
https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf
http://www.cs.nott.ac.uk/~psznza/G52PAS/lecture9.pdf
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture10FinalPart1.pdf
https://artint.info/html/ArtInt_206.html
http://www.cs.umd.edu/~djacobs/CMSC828/ApplicationsHMMs.pdf
https://www.quora.com/What-are-some-applications-of-Probabilistic-Graphical-Models
https://www.youtube.com/watch?time_continue=33&v=J_lXNPRIwag
http://www.cs.cmu.edu/~15464-s13/lectures/lecture6/IK.pdf
http://www.cs.columbia.edu/~allen/F15/NOTES/jacobians.pdf
https://wiki.python.org/moin/UsingPickle
https://distill.pub/2016/augmented-rnns/
https://indico.io/blog/sequence-modeling-neural-networks-part2-attention-models/
https://blog.keras.io/building-autoencoders-in-keras.html
https://arxiv.org/pdf/1511.06309.pdf
https://pjreddie.com/darknet/yolo/
http://cs231n.github.io/classification/
https://towardsdatascience.com/deep-learning-for-image-classification-why-its-challenging-where-we-ve-been-and-what-s-next-93b56948fcef
https://blog.paralleldots.com/data-science/must-read-path-breaking-papers-about-image-classification/
https://blog.openai.com/adversarial-example-research/
https://blog.xix.ai/how-adversarial-attacks-work-87495b81da2d
http://robots.stanford.edu/papers/thrun.robust-mcl.pdf
https://ai.googleblog.com/2018/12/exploring-quantum-neural-networks.html
https://realsense.intel.com/deep-learning-for-vr-ar/
http://vision.stanford.edu/pdf/mandlekar2018corl.pdf
http://robot.cc/papers/thrun.graphslam.pdf
http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
https://s3-us-west-1.amazonaws.com/udacity-drlnd/bookdraft2018.pdf
https://github.com/udacity/rl-cheatsheet/blob/master/cheatsheet.pdf
https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision
https://movingai.com/astar-var.html
http://theory.stanford.edu/~amitp/GameProgramming/Variations.html
https://www.cs.cmu.edu/~maxim/files/pathplanforMAV_icra13.pdf
https://arxiv.org/pdf/1611.03673.pdf
http://proceedings.mlr.press/v48/mniha16.pdf
https://deepmind.com/blog/article/reinforcement-learning-unsupervised-auxiliary-tasks
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c-c88f72a5e9f2
https://arxiv.org/pdf/1609.05143.pdf
https://openai.com/blog/ingredients-for-robotics-research/
https://arxiv.org/pdf/1708.05866.pdf
https://openai.com/resources/
https://www.groundai.com/project/self-supervised-deep-reinforcement-learning-with-generalized-computation-graphs-for-robot-navigation/
http://raiahadsell.com/uploads/3/6/4/2/36428762/erf2017_keynote_talk.pdf
https://papers.nips.cc/paper/1999/file/54f5f4071faca32ad5285fef87b78646-Paper.pdf
http://read.pudn.com/downloads142/sourcecode/others/617477/inventory%20supply%20chain/04051310570412465(1).pdf
https://deeplearning.mit.edu/
https://www.youtube.com/channel/UCXZCJLdBC09xxGZ6gcdrc6A
https://github.com/openai/gym
https://www.youtube.com/watch?v=iX5V1WpxxkY
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://www.youtube.com/watch?v=UNmqTiOnRfg
https://www.youtube.com/watch?v=WCUNPb-5EYI
http://twistedoakstudios.com/blog/Post554_minkowski-sums-and-differences
https://www.toptal.com/game/video-game-physics-part-ii-collision-detection-for-solid-objects
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Sir Sebastian course: AI for Robotics
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Interesting content on SLAM
facebook & SLAM: Bringing art to life through technology
facebook & SLAM: Powered by AI: Oculus Insight
SLAM (Simultaneous Localization And Mapping) Tracking Technology As Explained By Facebook
Mapping roads through deep learning and weakly supervised training
BatSLAM: Biomimetic Sonar SLAM
Which problems/situations are still unsolved or difficult in SLAM? -
Recent Papers on SLAM
ViTa-SLAM: Biologically-Inspired Visuo-Tactile SLAM
k -SLAM: A fast RGB-D SLAM approach for large indoor environments
k-SLAM: A fast RGB-D SLAM approach for large indoor environments - Video
DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot
Linear SLAM: Linearising the SLAM problems using submap joining
Benchmark of Visual SLAM Algorithms: ORB-SLAM2 vs RTAB-Map
GSLAM: A General SLAM Framework and Benchmark
miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
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External Material
ROS Navigation tuning guide
set start position of robot within amcl
How to Tune Navigational Parameters Using a Graphical Tool?
official AMCL documentation -
Mapping Accuracy
Loop closure during SLAM
Simultaneous Localization And Mapping (SLAM) using RTAB-Map
Occupancy Mapping. What is it?
Occupancy Grid Mapping - Log odd Update
Occupancy Grid Mapping - Handling Range Sensor
Inverse Sensor Model
https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
https://arxiv.org/pdf/1607.00133.pdf
https://blog.openmined.org/federated-learning-of-a-rnn-on-raspberry-pis/ from Sarah.
https://towardsdatascience.com/pysyft-android-b28da47a767e
https://course.fast.ai/
CNN's for Visual Recognition
Deep Conv nets for image classification
Large Scale image Recognition using DNN's
Transfer Learning
Awesome Deep Learning Papers
https://machinelearningmastery.com/statistical-hypothesis-tests/
https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/
https://www.datacamp.com/community/tutorials/finance-python-trading
https://towardsdatascience.com/inferential-statistics-series-t-test-using-numpy-2718f8f9bf2f
https://www.zipline.io/appendix.html#zipline.pipeline.factors.Factor.rank
https://towardsdatascience.com/how-the-mathematics-of-fractals-can-help-predict-stock-markets-shifts-19fee5dd6574
https://www.valgrind.org/info/
https://github.com/sowson/valgrind
https://docs.microsoft.com/en-us/visualstudio/debugger/finding-memory-leaks-using-the-crt-library?view=vs-2019
https://darkdust.net/files/GDB%20Cheat%20Sheet.pdf
https://github.com/hishamhm/htop/commit/da4877f48c70f765f8bfb60c7668e8499055662e
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#S-glossary
https://en.cppreference.com/w/cpp/language/identifiers
https://github.com/CppCon
https://en.cppreference.com/w/cpp/language/raii
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#rsmart-smart-pointers
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