m. de cecco - lucidi del corso di robotica e sensor fusion laser range finder camera direct depth...

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


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


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


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


Programma - sensor fusion + tesina


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


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


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 °


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


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m. de cecco - lucidi del corso di robotica e sensor fusion laser range finder camera direct depth...

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