Real time collision detection

https://github.com/bulletphysics/bullet3

http://www.r-5.org/files/books/computers/algo-list/realtime-3d/Christer_Ericson-Real-Time_Collision_Detection-EN.pdf

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Deep Learning Satellite Images

http://blog.kaggle.com/2017/04/26/dstl-satellite-imagery-competition-1st-place-winners-interview-kyle-lee/

https://deepsense.io/deep-learning-for-satellite-imagery-via-image-segmentation/

Securing FPGA

https://www.microsemi.com/products/fpga-soc/security/secure-boot-fpga

http://www.tortugalogic.com/blog/2016/8/2/securing-fpgas

https://www.altera.com/en_US/pdfs/literature/wp/wp-01033.pdf

http://www.springer.com/gp/book/9789048191567

http://www.springer.com/gp/book/9789400713376

https://www.blackhat.com/presentations/bh-usa-04/bh-us-04-grand/grand_embedded_security_US04.pdf

https://www.altera.com/content/dam/altera-www/global/en_US/pdfs/literature/wp/wp-01252-secure-device-manager-for-fpga-soc-security.pdf

 

CNN on FPGA

https://www.xilinx.com/products/boards-and-kits/ek-z7-zc702-g.html

https://arxiv.org/pdf/1701.03534.pdf

http://www.unmannedsystemstechnology.com/2017/05/cadence-announces-neural-network-dsp-ip-core

http://jaewoong.org/pubs/fpga17-next-generation-dnns.pdf

http://www.nallatech.com/fpga-acceleration-convolutional-neural-networks/

http://cadlab.cs.ucla.edu/~cong/slides/HALO15_keynote.pdf

https://github.com/dgschwend/zynqnet

https://www.xilinx.com/support/documentation/white_papers/wp399_Auto_DA_Systems.pdf

https://www.xilinx.com/applications/automotive.html

http://www.analog.com/en/analog-dialogue/articles/fpga-based-system-combines-two-streams.html

https://thenewstack.io/developers-fpgas-cloud/

https://github.com/bigsnarfdude/jetson-inference

https://www.xilinx.com/support/documentation/white_papers/wp399_Auto_DA_Systems.pdf

http://www.ti.com/lit/an/slyt702/slyt702.pdf

https://www.patreon.com/18F4550videos

https://www.xilinx.com/applications/automotive.html

ADAS

https://training.ti.com/sites/default/files/docs/CVPR_CV_FOR_ADAS_2016_v3.pdf

Self Driving Car Operating System

TACC Traffic-Aware-Cruise-Control

Traffic-Aware Cruise Control uses a camera

Mounted on the windshield behind the interior rear view mirror and a radar sensor in the

center of the front grill to detect whether there is a vehicle in front of you in the same

lane. If the area in front of car is clear, Traffic-Aware Cruise Control is designed to

drive consistently at a set speed.

When a vehicle is detected, Traffic-Aware Cruise Control is designed to slow down the car if

needed to maintain a selected time-based distance from the vehicle in front, up to the

set speed. Traffic-Aware Cruise Control does not eliminate the need to watch the road in

front of you and to apply the brakes if needed.

Traffic-Aware Cruise Control makes it easy to maintain a consistent time-based distance

from a vehicle travelling in front of you in the same lane. Traffic-Aware Cruise Control is

primarily intended for driving on dry, straight roads, such as highways and freeways. It

should not be used on city streets.

https://github.com/matthewzimmer/CarND-Extended-Kalman-Filter-Project

https://github.com/matthewzimmer/CarND-MiniFlow

https://github.com/awbrown90/CarND-Unscented-Kalman-Filter-Project

https://github.com/awbrown90/CarND-Extended-Kalman-Filter-Project

https://github.com/JonathanCMitchell/speedChallenge/blob/master/NvidiaModel-OpticalFlowDenseM2_8Epoch.ipynb

https://github.com/utiasSTARS/pykitti

https://medium.com/@jmitchell1991/autonomous-vehicle-speed-estimation-from-dashboard-cam-ca96c24120e4#.ls6vbiyuk

http://home.wlu.edu/~levys/kalman_tutorial/

https://github.com/simondlevy/TinyEKF

https://www.youtube.com/watch?v=P754S8F6ujo

Ego-motion

 

https://github.com/xuhuazhe/Egomotion/tree/master/visualization

https://thoth.inrialpes.fr/src/deepflow/

https://github.com/bigsnarfdude/epicflow

http://elvera.nue.tu-berlin.de/typo3/files/1366Senst2012.pdf

https://github.com/ju59199nahpx/General-Descriptors-in-CV

http://lmb.informatik.uni-freiburg.de/Publications/2010/Bro10e/

https://hal.inria.fr/hal-01142656/file/cvpr15_epicflow.pdf

http://www.itspy.cz/wp-content/uploads/2014/05/acmspy2012_submission_40.pdf

https://hal.archives-ouvertes.fr/hal-01010997/file/On_Modeling_Ego-motion_Uncertainty_for_Moving_Objects_Detection_from_a_Moving_Platform_IV2014_finalversion.pdf

https://lear.inrialpes.fr/people/wang/download/dense_trajectory_release_v1.2.tar.gz

https://thoth.inrialpes.fr/src/epicflow/

http://lear.inrialpes.fr/src/deepmatching/

https://github.com/bigsnarfdude/epicflow

http://elvera.nue.tu-berlin.de/typo3/files/1366Senst2012.pdf

http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.html

https://hal.inria.fr/hal-01142656/file/cvpr15_epicflow.pdf

https://github.com/ju59199nahpx/General-Descriptors-in-CV

Optical Flow experiments

https://arxiv.org/abs/1612.07695

https://arxiv.org/abs/1703.09771 6DOF tracking

Visual Detection, Recognition and Tracking with Deep Learning from Yu Huang

Filtering noise with Kalman Filters

The workhorse of robotics is Kalman Filters

http://greg.czerniak.info/guides/kalman1/

Training Neural Networks for classification using the Extended Kalman Filter: A comparative study (effen paywalled)

http://www.frc.ri.cmu.edu/~alonzo/pubs/reports/kalman_V2.pdf

https://github.com/iqans/opencv-python/blob/master/kalman.py

https://arxiv.org/abs/1703.02310