1. Labs/People/Organizations
1.1. Imperial
1.1.1. Andrew Davis
1.2. Oxford
1.3. ETH
1.3.1. Lorentz Mayer
1.4. GRASP Lab
1.5. Andrew Zisserman, BMVA Distinguished Fellow
1.6. http://raffaello.name/
1.7. http://www.osrfoundation.org/
1.8. Boston Dymanics Atlas/PETMAN
1.9. Willow Garage
2. Learning(Everything)
2.1. Computer Vision Course
2.2. SLAM Lecture by Andrew Davis
2.3. Visual Navigation for Flying Robots (Dr. Jürgen Sturm)
2.4. https://www.youtube.com/user/WillowGaragevideo
2.5. https://www.youtube.com/user/sparkfun
2.6. https://www.youtube.com/user/makemagazine
2.7. https://www.youtube.com/user/EEVblog
2.8. Semantic Object Recognition
2.9. ROS Channel (My YouTube)
2.10. ROS Industrial Consortium
2.11. Imperial Lecture Notes (Dropbox)
2.12. MilfordRobotics (YouTube)
2.13. SLAM Lectures
2.14. Bayes
2.14.1. https://www.youtube.com/channel/UCjtrtD-c6i2PxNiYs1BcJRg
3. SBC to APM via Mavlink - Is it Possible?
4. neuromorphic
5. COG
5.1. Visual Search
6. Electronics
6.1. Curent Draw
7. Integration
7.1. Semantic & Cognitive Robotics
7.1.1. RoboEarth
7.2. Emotiv EEG Control
8. Hardware
8.1. Vision Sensor
8.1.1. Carmine and Capri from Prime Sense
8.1.2. CCD vs CMOS Sensors
8.1.3. Structured light
8.1.4. time-of-flight camera
8.1.5. Shutter
8.1.5.1. Global Shutter
8.1.5.2. Rolling Shutter
8.1.6. Pixhawk's
8.1.6.1. Matrix Vision
8.1.6.2. e-Con
8.1.7. exposure/shutter speed
8.1.8. Odroid's Cam
8.2. Drone
8.2.1. 3DR RTF X8 2013
8.3. SBC
8.3.1. Gumstix
8.3.2. http://beagleboard.org/
8.3.3. ODroid U3 (Used by Pixhawk
8.3.4. ODroid
8.3.4.1. RobRoy Mag Editor
8.3.5. SBC Comparison
8.3.5.1. https://en.wikipedia.org/wiki/Comparison_of_single-board_computers
8.3.6. SBC Power
9. Math/Other
9.1. stereo matching
9.2. Bundle Adjustment
9.3. odometry
9.4. Model Theory
9.5. Regression
9.6. RANSAC
9.7. Group Theory
9.8. graph estimation
9.9. Relation
9.10. Equivalence Relation
9.11. Covariance Matrix
9.12. Disparity estimation
9.13. drift
10. Computer Vision
10.1. SLAM
10.1.1. PTAMM
10.1.2. JPL/NASA SLAM Use
10.1.3. MilfordRobotics
10.1.3.1. RatSLAM
10.1.3.2. SeqSlam
10.1.4. PTAM
10.1.5. Monocular SLAM and Real-Time Scene Perception - Andrew Davidson (Imperial)
10.1.6. MonoSLAM
10.1.7. IMU-VSLAM
10.1.8. SLAM++
10.1.9. why filter - network equivalent of kalman filter and its advantages (see MONOSLAM lecture, imperial)
10.1.10. loop closing event
10.1.11. SeqSLAM
10.1.12. slam is a joint estimation problem
10.2. TDL/Predator
10.3. Fundamental matrix (computer vision)
10.4. Active Vision
10.5. Occupancy Mapping (OcMaps) Place recognition (Imperial Notes
11. 3DR Arducopter
11.1. APM 2.6
11.2. https://code.google.com/p/ardupirates/
12. Fra
13. Pixhawk Middleware - MAVCONN Aerial Middleware
13.1. MIT's LCM
13.2. low-latency: Communication between processes is done in about 100 microseconds
13.3. Use of one protocol (MAVLink) on all subsystems (Linux, IMU, ground control)
13.4. ethzasl_sensor_fusion
14. Networking
14.1. UK
14.1.1. London
14.1.1.1. Nick Weldin (East London), R.O.S Lecture
15. Similar Projects (to Droidworx)
15.1. icarus-uav-system
15.2. Marcin
15.3. Projects USING PTAM
15.4. https://icoderaven.wordpress.com/tag/arducopter/
15.5. sFLY
16. MavProxy
17. Control Theory
17.1. Monte Carlo Simulation
17.2. Frequency Domain Analysis
17.3. loop closure
17.4. Time Domain Analysis
17.5. markov networks
17.6. Phase Portrait
17.7. controll loops and delay tolerances
17.8. EKF Kalman Filter
17.9. Topological Equivalence
17.10. Statistical Mechanics
17.11. Limit Cycle
17.12. Topological conjugacy
17.13. Phase Space
17.14. Laplace Transform
17.15. LEARNING
17.15.1. Brian (YouTube)
17.15.1.1. Broad Concepts in Control Theory
17.16. State estimation
18. ROS
18.1. ROS is not Real-Time(RTOS)
18.2. MAVLINK
18.3. RosCopter
18.4. ROS PTAM Package
18.4.1. ethzasl_ptam
18.4.1.1. Stephan Weiss
18.4.1.2. Papers
18.4.1.3. Camera functions well at 70m altitude sFly Test (ros package page)