m. de cecco - lucidi del corso di robotica e sensor fusion laser range finder camera direct depth...
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M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Laser Range Finder Laser Range Finder CameraCamera
direct depth measurementdirect depth measurement
wide accuracy span (till 200 m)wide accuracy span (till 200 m)
only 2 or 3 D contouronly 2 or 3 D contour
illumination dependentillumination dependent
accurate only for limited distancesaccurate only for limited distances
info on colour and textureinfo on colour and texture
high computational timehigh computational time
SENSOR FUSION - Laser and Camera
Programma - LASER + CAMERA
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
MEASUREMENT BY LASER and CAMERA
• Laser rangefinders, principles and applications
• Laser-Camera Calibration
MEASUREMENT BY LASER and CAMERA: object recognition
• Clustering and segmentation of the scene seen by the laser
• Chamfer distance (or Hausdorff)
MEASUREMENT BY LASER and CAMERA: object recognition
• reprojection of the object model of CCD
• Corner extraction
• Matching and acceptance
Programma - LASER + CAMERA
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
MEASUREMENT BY LASER and CAMERA: object recognition• Practice with real data. The scene will be a box of given size to be recognizedMEASUREMENT BY LASER and CAMERA: object recognition• Practice with real data.SUPERQUADRICHE• General conceptsSUPERQUADRICHE• Application to object recognition
Programma - esercitazione
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
SENSOR FUSION of timeline signals- Complementary Filtering. Theory and applications. Example of simulation of an altimeter baro-inertial.SENSOR FUSION of timeline signals- Simulation PC in the classroom portion of the estimate by filtering between a barometer and an inertial platformSENSOR FUSION of timeline signals- Use of real data:- Measurement of the camera position by means of an object in motion on a plane by means of KLT, after having calibrated the worktop (using a grid placed on the floor)- Combined with the accelerometer data and complementary filtering
Telecamera + oggetto sul piano con accelerometro solidale
Programma - sensor fusion + esercitazione
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - sensor fusion + tesina
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
SENSOR FUSION
- Statistical concepts accessories, Bayes' Theorem
SENSOR FUSION
- Application of Bayes' theorem to the fusion of information scalar and vector
SENSOR FUSION
- Kalman Filter
SENSOR FUSION. Tutorial SLAM + Kalman. Mapping with laser scanner or camera
SENSOR FUSION. Tutorial SLAM + Kalman. Mapping with laser scanner or camera
Programma - sensor fusion + tesina
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
MOBILE ROBOT - Overview of applications. Localization issues, planning and
control, holonomic and non-linear differential constraints. Conditions of integrability,
Model Differential Drive. Recursive equations for odometry.
MOBILE ROBOT - Models kinematic unicycle, bicycle and bicycle trailers with N
MOBILE ROBOT - Problem of planning. Classification. Transformation of kinematic
models in chained form.
MOBILE ROBOT - Planning open-loop. Systems in chained form for the solution of
the motion point-to-point with sinusoidal input, wise constant, polynomial. Calculation
of Cartesian trajectories eligible
MOBILE ROBOT - Planning open-loop. Clothoids and polar spline. Examples of
calculation.
MOBILE ROBOT - Controllability of systems that are not holonomic. Example of
control system in chained form linearized around the desired trajectory
Programma - robot mobili
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Modalità di esame
Exam: homework + 1 ORAL ARGUMENTS ON 2 CHOICES (between 4 topics, which does not coincide with that of the homework), [NOTE: 1 topic for mehanics area]
Homework chose examples: trajectory control of manipulators by inverting the differential kinematics (CLASS) simulation and trajectory control for non-holonomic vehicles processing data for the calibration kinematics of an autonomous vehicle AGV SLAM using a laser scanner at 360 °
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
L.Sciavicco, B. Siciliano, Robotica - Modellistica, pianificazione e controllo 3/ed, McGraw
Mitchell Harvey, "Multi-Sensor Data Fusion: An Introduction" - Springer 2007
Ake Bjork, Numerical methods for least squares problems
M. De Cecco, Lucidi del corso di Robotica e Sensor Fusion
Luca Baglivo, M. De Cecco, Navigazione di Veicoli Autonomi - Fondamenti di “sensor fusion” per la localizzazione
L. Baglivo, Navigazione di Veicoli Autonomi (Localizzazione, Pianificazione e Controllo traiettoria)
Testi Consigliati
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
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