Objectives:
The
work starts by estimating position, orientation, and intrinsic parameters
(calibration) of each camera mounted on a car. The cameras may have
non-overlapping fields of view (FOVs). Calibration comprises both the intrinsic
and extrinsic parameters. The non-overlapping FOVs are overcome by placing calibration
patterns at known poses or by the motion of the car.
Given
the calibration of the cameras, objects imaged by multiple cameras can be
reconstructed in 3D. The 3D poses of objects (obstacles) are useful to estimate
obstacle avoidance trajectories. Corresponded points of moving objects allow
also to estimate time-to-contact information. An informal discussion on the use
of sensors for autonomous vehicles navigation can be found in:
https://www.quora.com/Why-do-self-driving-cars-use-LIDAR-instead-of-depth-cameras-like-Kinect
Steering
and speed control need to be adapted to the perceived environment. Trajectories
planning and following are an essential component to develop upon the sensing
devices.
Work-proposal
detailed steps:
-
Review the state-of-art on the calibration of multiple cameras on a car
-
Setup of a simulation scenario based on applied games
-
Calibration of the cameras
-
Steering for obstacle avoidance
This
work is conducted within the framework of the project VIENA, driving a FIAT
electric car, representing the IST. Some information regarding the VIENA
project can be found in
https://istpowerlab.wixsite.com/e2tes/viena
References:
[Silva12]
M. Silva, R. Ferreira and J. Gaspar. Camera Calibration using a Color-Depth
Camera: Points and Lines Based DLT including Radial Distortion. In WS in
Color-Depth Camera Fusion in Robotics, held with IROS 2012.
[Hartley00]
R. I. Hartley and I. Zisserman. Multiple view geometry in computer vision.
pages 150–152, 2000.
Localization:
ISR/IST
-- More information in:
http://www.isr.tecnico.ulisboa.pt/~jag