scholarly journals Vision-based dynamic target trajectory and ego-motion estimation using incremental light bundle adjustment

2018 ◽  
Vol 10 (2) ◽  
pp. 157-170 ◽  
Author(s):  
Michael Chojnacki ◽  
Vadim Indelman
Keyword(s):  
Motion Estimation ◽  
Target Tracking ◽  
Moving Objects ◽  
3D Structure ◽  
Computational Effort ◽  
Bundle Adjustment ◽  
Computational Time ◽  
Computationally Efficient ◽  
Dynamic Target ◽  
Real Imagery

This paper presents a vision-based, computationally efficient method for simultaneous robot motion estimation and dynamic target tracking while operating in GPS-denied unknown or uncertain environments. While numerous vision-based approaches are able to achieve simultaneous ego-motion estimation along with detection and tracking of moving objects, many of them require performing a bundle adjustment optimization, which involves the estimation of the 3D points observed in the process. One of the main concerns in robotics applications is the computational effort required to sustain extended operation. Considering applications for which the primary interest is highly accurate online navigation rather than mapping, the number of involved variables can be considerably reduced by avoiding the explicit 3D structure reconstruction and consequently save processing time. We take advantage of the light bundle adjustment method, which allows for ego-motion calculation without the need for 3D points online reconstruction, and thus, to significantly reduce computational time compared to bundle adjustment. The proposed method integrates the target tracking problem into the light bundle adjustment framework, yielding a simultaneous ego-motion estimation and tracking process, in which the target is the only explicitly online reconstructed 3D point. Our approach is compared to bundle adjustment with target tracking in terms of accuracy and computational complexity, using simulated aerial scenarios and real-imagery experiments.

scholarly journals EVALUATION OF SIFT AND SURF FOR VISION BASED LOCALIZATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 685-692 ◽  
Author(s):  
Xiaozhi Qu ◽  
Bahman Soheilian ◽  
Emmanuel Habets ◽  
Nicolas Paparoditis
Keyword(s):  
Autonomous Navigation ◽  
Bundle Adjustment ◽  
Interest Points ◽  
Point Distribution ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Vision Based Localization ◽  
Local Bundle Adjustment ◽  
The Impact ◽  
And Robotics

Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT.

scholarly journals EVALUATION OF SIFT AND SURF FOR VISION BASED LOCALIZATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 685-692 ◽  
Author(s):  
Xiaozhi Qu ◽  
Bahman Soheilian ◽  
Emmanuel Habets ◽  
Nicolas Paparoditis
Keyword(s):  
Autonomous Navigation ◽  
Bundle Adjustment ◽  
Interest Points ◽  
Point Distribution ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Vision Based Localization ◽  
Local Bundle Adjustment ◽  
The Impact ◽  
And Robotics

Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT.

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