EVALUATION OF SIFT AND SURF FOR VISION BASED LOCALIZATION

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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CHARACTERIZATION OF A MOBILE MAPPING SYSTEM FOR SEAMLESS NAVIGATION

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

10.5194/isprs-archives-xliii-b1-2020-227-2020 ◽

2020 ◽

Vol XLIII-B1-2020 ◽

pp. 227-234 ◽

Cited By ~ 1

Author(s):

V. Di Pietra ◽

N. Grasso ◽

M. Piras ◽

P. Dabove

Keyword(s):

Autonomous Navigation ◽

Estimation Algorithm ◽

Mobile Mapping ◽

Trajectory Estimation ◽

Sensor Platform ◽

Mapping System ◽

Global Consistency ◽

Mobile Mapping System ◽

Correct Weight

Abstract. Mobile Mapping Systems (MMS) are multi-sensor technologies based on SLAM procedure, which provides accurate 3D measurement and mapping of the environment as also trajectory estimation for autonomous navigation. The major limits of these algorithms are the navigation and mapping inconsistence over the time and the georeferencing of the products. These issues are particularly relevant for pose estimation regardless the environment like in seamless navigation. This paper is a preliminary analysis on a proposed multi-sensor platform integrated for indoor/outdoor seamless positioning system. In particular the work is devoted to analyze the performances of the MMS in term of positioning accuracy and to evaluate its improvement with the integration of GNSS and UWB technology. The results show that, if the GNSS and UWB signal are not degraded, using the correct weight to their observations in the Stencil estimation algorithm, is possible to obtain an improvement in the accuracy of the MMS navigation solution as also in the global consistency of the final point cloud. This improvement is measured in about 7 cm for planimetric coordinate and 34 cm along the elevation with respect to the use of the Stencil system alone.

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ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY

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

10.5194/isprs-archives-xli-b3-511-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 511-518

Author(s):

J. Kersten ◽

V. Rodehorst

Keyword(s):

Outlier Detection ◽

Autonomous Navigation ◽

Visual Odometry ◽

Bundle Adjustment ◽

Unmanned Aircraft ◽

Estimation Errors ◽

Keyframe Selection ◽

Reduction Strategies ◽

The Impact ◽

Stereo Visual Odometry

Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements.

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ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY

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

10.5194/isprsarchives-xli-b3-511-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 511-518

Author(s):

J. Kersten ◽

V. Rodehorst

Keyword(s):

Outlier Detection ◽

Autonomous Navigation ◽

Visual Odometry ◽

Bundle Adjustment ◽

Unmanned Aircraft ◽

Estimation Errors ◽

Keyframe Selection ◽

Reduction Strategies ◽

The Impact ◽

Stereo Visual Odometry

Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements.

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ROBUST AND ACCURATE IMAGE-BASED GEOREFERENCING EXPLOITING RELATIVE ORIENTATION CONSTRAINTS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-57-2018 ◽

2018 ◽

Vol IV-2 ◽

pp. 57-64

Author(s):

S. Cavegn ◽

S. Blaser ◽

S. Nebiker ◽

N. Haala

Keyword(s):

Test Site ◽

Urban Environments ◽

Relative Orientation ◽

Bundle Adjustment ◽

Test Field ◽

Mobile Mapping ◽

Mapping System ◽

Mobile Mapping System ◽

Accuracy Increase ◽

Orientation Parameters

Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2&thinsp;cm in an indoor area, and 3&thinsp;cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2&ndash;3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.

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VERIFICATION OF THE RELATIONSHIP BETWEEN ADJUSTMENT EFFECT BY THE GROUND CONTROL POINT AND DRIVING SPEED OF MOBILE MAPPING SYSTEM

Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) ◽

10.2208/jscejcei.72.ii_31 ◽

2016 ◽

Vol 72 (2) ◽

pp. II_31-II_39

Author(s):

Naoki OKAMOTO ◽

Tatsunori SADA ◽

Hisashi EMORI

Keyword(s):

Control Point ◽

Ground Control ◽

Mobile Mapping ◽

Ground Control Point ◽

Driving Speed ◽

Mapping System ◽

Mobile Mapping System ◽

The Relationship

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Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽

10.3390/s21082595 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2595

Author(s):

Balakrishnan Ramalingam ◽

Abdullah Aamir Hayat ◽

Mohan Rajesh Elara ◽

Braulio Félix Gómez ◽

Lim Yi ◽

...

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

High Accuracy ◽

Experimental Results ◽

Mobile Mapping ◽

Mapping System ◽

Mobile Mapping System ◽

Reconfigurable Robot ◽

Nvidia Gpu ◽

Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

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