- Išsiųsime per 10–14 d.d.
/images/books/4534840/9780863414534.jpg)
- Autorius: J Kolodko
- Leidėjas: Institution of Engineering & Technology
- ISBN-10: 0863414532
- ISBN-13: 9780863414534
- Formatas: 15.6 x 23.4 x 2.5 cm, kieti viršeliai
- Kalba: Anglų
- Extra -15 % nuolaida šiai knygai su kodu: ENG15
Atsiliepimai
Aprašymas
Segmenting the environment surrounding an autonomous vehicle into coherently moving regions is a vital first step towards intelligent autonomous navigation. Without this temporal information, navigation becomes a simple obstacle avoidance scheme that is inappropriate in highly dynamic environments such as roadways and places where many people congregate.
The book begins by looking at the problem of motion estimation from biological, algorithmic and digital perspectives. It goes on to describe an algorithm that fits with the motion processing model, and hardware and software constraints. This algorithm is based on the optical flow constraint equation and introduces range information to resolve the depth-velocity ambiguity, which is critical for autonomous navigation. Finally, implementation of the algorithm in digital hardware is described in detail, covering both the initial motion processing model and the chosen hardware platforms, and the global functional structure of the system.
EXTRA 15 % nuolaida su kodu: ENG15
Akcija baigiasi už 5d.12:38:14
Nuolaidos kodas galioja perkant nuo 10 €. Nuolaidos nesumuojamos.
- Autorius: J Kolodko
- Leidėjas: Institution of Engineering & Technology
- ISBN-10: 0863414532
- ISBN-13: 9780863414534
- Formatas: 15.6 x 23.4 x 2.5 cm, kieti viršeliai
- Kalba: Anglų
Segmenting the environment surrounding an autonomous vehicle into coherently moving regions is a vital first step towards intelligent autonomous navigation. Without this temporal information, navigation becomes a simple obstacle avoidance scheme that is inappropriate in highly dynamic environments such as roadways and places where many people congregate.
The book begins by looking at the problem of motion estimation from biological, algorithmic and digital perspectives. It goes on to describe an algorithm that fits with the motion processing model, and hardware and software constraints. This algorithm is based on the optical flow constraint equation and introduces range information to resolve the depth-velocity ambiguity, which is critical for autonomous navigation. Finally, implementation of the algorithm in digital hardware is described in detail, covering both the initial motion processing model and the chosen hardware platforms, and the global functional structure of the system.
Atsiliepimai