Abstract
For their complete realization, autonomous vehicles (AVs) fundamentally rely on the Global Navigation Satellite System (GNSS) to provide positioning and navigation information. However, in area such as urban cores, parking lots, and under dense foliage, which are all commonly frequented by AVs, GNSS signals suffer from blockage, interference, and multipath. These effects cause high levels of errors and long durations of service discontinuity that mar the performance of current systems. The prevalence of vision and low-cost inertial sensors provides an attractive opportunity to further increase the positioning and navigation accuracy in such GNSS-challenged environments. This paper presents enhancements to existing multisensor integration systems utilizing the inertial navigation system (INS) to aid in Visual Odometry (VO) outlier feature rejection. A scheme called Aided Visual Odometry (AVO) is developed and integrated with a high performance mechanization architecture utilizing vehicle motion and orientation sensors. The resulting solution exhibits improved state covariance convergence and navigation accuracy, while reducing computational complexity. Experimental verification of the proposed solution is illustrated through three real road trajectories, over two different land vehicles, and using two low-cost inertial measurement units (IMUs).
Highlights
As part of Intelligent Transport Systems (ITS), autonomous vehicles (AVs) are expected to become the norm in the near future; examples include self-driving taxis, autonomous public services, and AV ride sharing
This paper aims at the design and realization of an integrated multisensor positioning and navigation system capable of seamless and reliable positioning in all environments for land vehicles
The camera captured video at 30 fps, the Visual Odometry (VO) algorithm was run at 5 Hz, owing to the increased computational demand of higher update rates; the VO localization estimates were
Summary
As part of Intelligent Transport Systems (ITS), autonomous vehicles (AVs) are expected to become the norm in the near future; examples include self-driving taxis (implemented by companies such as nuTonomy in Singapore [1] and Uber), autonomous public services, and AV ride sharing. Such AV applications are predicted to reduce traffic congestions, improve passenger safety, and cut down overall operating costs [2]. Examples of such outage situations include urban centers, tunnels, parking lots, and dense foliage
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