國立虎尾科技大學 |

Integrated visual odometry for improved autonomous vehicle navigation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Integrated visual odometry for improved autonomous vehicle navigation./
Author:	Rahman, Muhammed Tahsin.
Description:	1 online resource (135 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 75-01C.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)

Integrated visual odometry for improved autonomous vehicle navigation.
Rahman, Muhammed Tahsin.

Integrated visual odometry for improved autonomous vehicle navigation. - 1 online resource (135 pages)

Source: Dissertation Abstracts International, Volume: 75-01C.

Thesis (M.A.Sc.)--Queen's University (Canada), 2017.

Includes bibliographical references

Safe, efficient, and comfortable travel has always been a fundamental human necessity. From reducing commute time to saving lives, autonomous vehicles promise to be an indispensable tool in the age of modern urban transportation. However, implementing such a disruptive technology comes with significant challenges, a major one being accurate vehicle positioning and localization.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Integrated visual odometry for improved autonomous vehicle navigation.
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245 1 0 $a Integrated visual odometry for improved autonomous vehicle navigation.
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500 $a Source: Dissertation Abstracts International, Volume: 75-01C.
502 $a Thesis (M.A.Sc.)--Queen's University (Canada), 2017.
504 $a Includes bibliographical references
520 $a Safe, efficient, and comfortable travel has always been a fundamental human necessity. From reducing commute time to saving lives, autonomous vehicles promise to be an indispensable tool in the age of modern urban transportation. However, implementing such a disruptive technology comes with significant challenges, a major one being accurate vehicle positioning and localization.
520 $a In situations such as urban cores, parking lots, and under dense foliage, positioning information provided by the Global Navigation Satellite System (GNSS) deteriorates significantly. GNSS is increasingly integrated with other systems such as the inertial navigation system (INS) and Visual Odometry (VO) to bridge these outages. The major drawback of current integration systems is that they are unable to provide a stable navigation estimate during extended and frequent GNSS outages, situations common to autonomous vehicles.
520 $a To improve the overall system accuracy, this thesis presents a multi-sensor navigation solution that integrates GNSS with a low-cost inertial measurement unit (IMU) and VO. Firstly, a switching architecture is detailed, which implements a loosely coupled extended Kalman filter (EKF) that fuses VO updates only when GPS is unavailable, reducing computational resources while limiting the errors inherent in an INS. Secondly, termed R-AVO (Reduced-Aided Visual Odometry), the orientation and translation of a vehicle, as determined by a low drift INS algorithm, is used to evaluate which feature matches from the processed image frames are to be considered in VO. This method is shown to be both more accurate and less computationally expensive than classic feature outlier rejection schemes, which are iterative in nature. Reducing the computation complexity of VO paves the way for even more sensors to be utilized by a vehicle.
520 $a The developed algorithms are evaluated on several real road trajectories, including simulated outages that are as long as 10 minutes. To ensure generic implementations, two different land vehicles are used in the trajectories and two different IMUs are tested.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Automotive engineering. $3 1104081
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0540
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Queen's University (Canada). $3 1148613
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10671726 $z click for full text (PQDT)