luis mejias , srikanth saripalli , pascual campoy and gaurav sukhatme
DESCRIPTION
Luis Mejias , Srikanth Saripalli , Pascual Campoy and Gaurav Sukhatme. Visual Servoing of an Autonomous Helicopter in Urban Areas Using Feature Tracking presented by Wen Li. Outline. Introduction Related work Testbed Visual preprocessing Control Architectures Experiments - PowerPoint PPT PresentationTRANSCRIPT
Visual Servoing of an Autonomous Helicopter in Urban Areas Using Feature Tracking presented by Wen Li
Luis Mejias, Srikanth Saripalli, Pascual Campoy and Gaurav Sukhatme
Outline
Introduction Related work Testbed Visual preprocessing Control Architectures Experiments Conclusion
Outline
Introduction Related work Testbed Visual preprocessing Control Architectures Experiments Conclusion
Introduction
Goal: vision-guided autonomous flying robots
Application: Law enforcement, search and rescue,
inspection and surveillance Technique:
Object detection, tracking, inertial navigation, GPS and nonlinear system modeling
Introduction
In this paper: Two UAVs – Avatar and COLIBRI Visual tracking => control
commands
Outline
IntroductionRelated work
Testbed Visual preprocessing Control Architectures Experiments Conclusion
Related Work
Hummingbird (A. Conway, 1995) Model-scale Use GPS only 4 GPS antennas Precisions: position 1cm
attitude 1 degree
Related Work
AVATAR (Jun, 1999) Onboard INS & GPS Kalman Filter for State Estimation Simulation
Related Work
Vision-guided Helicopter (Amidi, 1996, 1997) Onboard DSP-based vision processor Combine GPS and IMU data
Related Work
Vision-augmented navigation system (Bosse, 1997) Uses vision in-the-loop to control a helicopter
Visual odometer (Amidi, 1998) A notable vision-based technique used in
autonomous helicopter (Wu, et al, 2005)
Vision is used as additional sensor and fused with inertial and heading measurements for control
Outline
Introduction Related work
Testbed Visual preprocessing Control Architectures Experiments Conclusion
Autonomous Helicopter Testbed AVATAR
Gas-powered radio-controlled model helicopter RT-2 DGPS system provides positional accuracy of
2 cm ISIS-IMU provides rate information to onboard
computer, which is fused using a 16 state Kalman filter
Ground station: a laptop to send high-level control commands and differential GPS corrections
Autonomous flight is achieved using a behavior-based control architecture
Autonomous Helicopter Testbed COLIBRI
Gas powered model helicopter Fitted with a Xscale based flight computer
augmented with GPS, IMU, Magnetometer, fused with a Kalman filter
VIA mini-ITX 1.25 GHz computer onboard with 512 Mb RAM, wireless interface and a firewire color camera
Ground station: a laptop to send high-level control commands, and for visualization
Outline
Introduction Related work Testbed
Visual preprocessing Control Architectures Experiments Conclusion
Visual Preprocessing -- AVATAR Image segmentation and
thresholding Convert the image to grayscale Use the value of “target color” as
threshold Segment the image to binary image
where the object of interest is represented by 1’s and background with 0’s
Visual Preprocessing -- AVATAR Square Finding
Find contours (represented by polylines) from the binary image
Use an algorithm to reduce the points in polylines
Result: simplified squares
Visual Preprocessing -- AVATAR Template Matching
User selects a detected window (a target)from the GUI
A patch is selected around the location of the target
Use local search window to find best match between the target and the detected contours, deciding which window to track
Visual Preprocessing -- AVATAR Kalman Filter
Once a suitable match is found, a Kalman filter is used to track the feature positions
Input: x and y coordinates of the features
Output: estimates of these coordinates in the next frame
Visual Preprocessing -- COLIBRI The user selects the object of
interest from the GUI The location of the object is used to
generate visual reference
Visual Preprocessing -- COLIBRI Lateral visual reference
Visual Preprocessing -- COLIBRI Vertical Visual Reference
Outline
Introduction Related work Testbed Visual preprocessing
Control Architectures Experiments Conclusion
Control Architectures -- AVATAR A hierarchical
behavior based control architecture
Output of Kalman filter is compared with desired values to give an error signal to controller
Control Architectures -- COLIBRI Controller is
based on a decoupled PID control
Outline
Introduction Related work Testbed Visual preprocessing Control Architectures
Experiments Conclusion
Experimental results
At Del Valle Urban Search and Rescue Training site in Santa Clarita, California
AVATAR, four trials First, the helicopter is commanded to
fly autonomously to a given GPS waypoint
As soon as it detects the featured window, the controller switches from GPS-based to vision-based control
Location of the features in the image
Helicopter position in meters. (left figure) vertical axis– easting
(right figure) vertical axis – northing
Experimental Results
At ETSII Campus in Madrid, Spain COLIBRI Seven experimental trials on two
different days
Velocity references (vyr) with the helicopter velocity (vy)
Lateral displacement (east)
Velocity references (vzr) with the helicopter velocity (vz)
altitude displacement (down)
Helicopter displacements during the entire flight trial
Video demonstration
colibrivideoWeb.wmv
Outline
Introduction Related work Testbed Visual preprocessing Control Architectures Experiments
Conclusion
Conclusion -- Authors
Demonstrated an approach to visually control an autonomous helicopter: use visual algorithm to command UAV when GPS has dropouts
Experimentally demonstrated by performing vision-based window tracking tasks on two different platforms at different locations and different conditions
Conclusion -- Personal
The topic is interesting Visual algorithm is demonstrated
effective in the experiments
But… the writing is so ugly. Poor explanation▪ features, template and matching
Incomplete explanation of figures