Fast Odometry Integration in Local Bundle Adjustment-Based Visual SLAM

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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EVALUATION OF SIFT AND SURF FOR VISION BASED LOCALIZATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-685-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 685-692 ◽

Cited By ~ 2

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.

Download Full-text