Vision-based Positioning with a Single Camera and 3D Maps: Accuracy and Reliability Analysis

2012 Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS)

2012

Self Cite

In this paper, we introduce multi-image matching using invariant features into the 3D mapping process for vision-based navigation applications. Our previously developed vision-based positioning system is modified accordingly, and experiments prove that the 3D map has been enhanced under a better imaging geometry. Consequently, the localization accuracy is much improved. Final positioning accuracy is around 0.2 m. The test also reveals some limitations: the system cannot perform well in areas where features are not rich or not included in the view; mismatches affect the positioning accuracy.

Section: System Methodologymentioning

confidence: 99%

Multi-image matching for 3D mapping in vision-based navigation applications

2012 Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS)

2012

Self Cite

“…In the framework of navigation using reality-based 3D maps (Li et al, 2011), this section aims to compare the navigation performance of LSIF/PCD with three other competitive algorithms (LICF/PCD, SIFT/SIFT and SIFT/PCD).…”

Section: Experiments and Analysismentioning

confidence: 99%

New Environmental Line Feature-based Vision Navigation: Design and Analysis

Chen

et al. 2017

J. Navigation

Self Cite

Vision navigation using environmental features has been widely applied when satellite signals are not available. However, the matching performance of traditional environmental features such as keypoints degrades significantly in weakly textured areas, deteriorating navigation performance. Further, the user needs to evaluate and assure feature matching quality. In this paper, a new feature, named Line Segment Intersection Feature (LSIF), is proposed to solve the availability problem in weakly textured regions. Then a combined descriptor involving global structure and local gradient is designed for similarity comparison. To achieve reliable point-to-point matching, a coarse-to-fine matching algorithm is developed, which improves the performance of the point set matching algorithm. Finally, a framework of matching quality evaluation is proposed to assure matching performance. Through the comparison, it is demonstrated that the proposed new feature has superior overall performance especially on correctly matched numbers of keypoints and matching correctness. Also, using real image sets with weak texture, it is shown that the proposed LSIF can achieve improved navigation solutions with high continuity and accuracy.

“…The image measurements captured by the camera can calibrate and confine the time-varying inertial errors. Meanwhile, the positioning of the images can also yield high precision when applied in a close-range area [ 2 , 3 ]. In this paper, a high-precision image-aided inertial navigation technology with close-range features deployed in the surrounding environment is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…After fusing with inertial sensors, the image-aided inertial navigation system is able to cover the limitations and deficiencies of a standalone system [ 6 – 9 ]. Features in the images are matched with absolute features in the real world whose coordinates are known in the navigation environment [ 2 , 3 , 10 ]. Absolute image-based navigation was used to aid the inertial navigation system (INS) in this study.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Image Aided Inertial Navigation with Known Features: Observability Analysis and Performance Evaluation

Jiang

Niu

et al. 2014

Sensors

A high-precision image-aided inertial navigation system (INS) is proposed as an alternative to the carrier-phase-based differential Global Navigation Satellite Systems (CDGNSSs) when satellite-based navigation systems are unavailable. In this paper, the image/INS integrated algorithm is modeled by a tightly-coupled iterative extended Kalman filter (IEKF). Tightly-coupled integration ensures that the integrated system is reliable, even if few known feature points (i.e., less than three) are observed in the images. A new global observability analysis of this tightly-coupled integration is presented to guarantee that the system is observable under the necessary conditions. The analysis conclusions were verified by simulations and field tests. The field tests also indicate that high-precision position (centimeter-level) and attitude (half-degree-level)-integrated solutions can be achieved in a global reference.