.png){kind=icon}
.png){kind=icon}
Return
Correlation-Based Visual Odometry for Ground Vehicles
.png){kind=icon}
PRE
AI
DOI：10.1002/rob.20407.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Reliable motion estimation is a key component for autonomous vehicles. We present a visual odometry method for ground vehicles using template matching. The method uses a downward-facing camera perpendicular to the ground and estimates the motion of the vehicle by analyzing the image shift from frame to frame. Specifically, an image region (template) is selected, and using correlation we find the corresponding image region in the next frame. We introduce the use of multitemplate correlation matching and suggest template quality measures for estimating the suitability of a template for the purpose of correlation. Several aspects of the template choice are also presented. Through an extensive analysis, we derive the expected theoretical error rate of our system and show its dependence on the template window size and image noise. We also show how a linear forward prediction filter can be used to limit the search area to significantly increase the computation performance. Using a single camera and assuming an Ackerman-steering model, the method has been implemented successfully on a large industrial forklift and a 4x4 vehicle. Over 6 km of field trials from our industrial test site, an off-road area and an urban environment are presented illustrating the applicability of the method as an independent sensor for large vehicle motion estimation at practical velocities. (C) 2011 Wiley Periodicals, Inc.
Keywords:
ROBUST
PERFORMANCE
NAVIGATION
TRACKING
FLOW
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.2
Papers:
1.7K
Citations:
6.0K