1 / 9

Obstacle Avoidance using Machine Vision

Obstacle Avoidance using Machine Vision. Joose Rautemaa 09455759. Introduction . A control system for a car that can avoid obstacles using Machine Vision D eveloped on and for a model car, could possibly be used in a real car too with minor modifications

waneta
Télécharger la présentation

Obstacle Avoidance using Machine Vision

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Obstacle Avoidance using Machine Vision Joose Rautemaa 09455759

  2. Introduction • A control system for a car that can avoid obstacles using Machine Vision • Developed on and for a model car, could possibly be used in a real car too with minor modifications • Hardware includes a Raspberry Pi and a Raspberry Pi Camera • Remote control capabilities over WLAN • Autonomous navigation will be based on a digital compass and GPS waypoints

  3. Aims • Relatively safe and fast autonomous travel • Recognizing obstacles with the camera allows avoiding them or going around them if possible • Using OpenCV for the recognition of objects • Ability to easily select the destination and also manually remotely control the car • A simple web interface that can be used with a laptop or a mobile phone over WLAN where a user can enter coordinates, select coordinates from a map, or manually control the car • The interface has an option for a direct camera feed

  4. Results • The Raspberry Pi is a very small and inexpensive device, but it is not hugely powerful. For faster object detection you would need a more powerful computer • The Raspberry Pi is very energy efficient which allows for much longer operating times if using electric power only • OpenCV is really reliable and efficient in recognizing objects in video feeds, however it requires a lot of processing power • GPS navigation might have to be on a separate device, for example on an Arduino that is attached via a serial link, because the Rasbperry Pi might not have enough prosessing power to handle that too.

  5. References • OpenCV documentation • Adafruit Learning Community • Various blogs and websites • Various forums and IRC channels • Peers and professionals

  6. What next? • Need to find a good model car that can be used as a base for actual testing of the control features • Need to start designing the GPS navigation system and the web interface • The latest version of OpenCV needs to be compiled for Raspberry Pi, this process might take up to 10 hours

  7. Pictures

More Related