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

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.

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

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.

scholarly journals Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization

2019 ◽  
Vol 11 (1) ◽  
pp. 67 ◽  
Author(s):  
Sung-Joo Yoon ◽  
Taejung Kim
Keyword(s):  
Pose Estimation ◽  
Automobile Industry ◽  
Visual Odometry ◽  
Geometric Interpretation ◽  
Bundle Adjustment ◽  
Processing Technologies ◽  
Extraction Step ◽  
Feature Optimization ◽  
Institute Of Technology ◽  
Stereo Visual Odometry

One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the construction cost is low. In this study, we developed stereo visual odometry (SVO) based on photogrammetric geometric interpretation. The proposed method performed feature optimization and pose estimation through photogrammetric bundle adjustment. After corresponding the point extraction step, the feature optimization was carried out with photogrammetry-based and vision-based optimization. Then, absolute orientation was performed for pose estimation through bundle adjustment. We used ten sequences provided by the Karlsruhe institute of technology and Toyota technological institute (KITTI) community. Through a two-step optimization process, we confirmed that the outliers, which were not removed by conventional outlier filters, were removed. We also were able to confirm the applicability of photogrammetric techniques to stereo visual odometry technology.

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.

scholarly journals Event-Based Stereo Visual Odometry

2021 ◽  
pp. 1-18
Author(s):  
Yi Zhou ◽  
Guillermo Gallego ◽  
Shaojie Shen
Keyword(s):  
Visual Odometry ◽  
Event Based ◽  
Stereo Visual Odometry

Impact of UAS with Low Size, Weight, and Power Sensors on Air Traffic Controllers’ Performance and Acceptability Ratings

2020 ◽  
Vol 64 (1) ◽  
pp. 154-158
Author(s):  
Kim-Phuong L. Vu ◽  
Jonathan VanLuven ◽  
Timothy Diep ◽  
Vernol Battiste ◽  
Summer Brandt ◽  
...  
Keyword(s):  
Air Traffic ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Subjective Ratings ◽  
Detection Range ◽  
Human In The Loop ◽  
Air Traffic Controllers ◽  
Low Altitude ◽  
Sector Performance ◽  
The Impact

A human-in-the-loop simulation was conducted to evaluate the impact of Unmanned Aircraft Systems (UAS) with low size, weight, and power (SWaP) sensors operating in a busy, low-altitude sector. Use of low SWaP sensors allow for UAS to perform detect-and-avoid (DAA) maneuvers against non-transponding traffic in the sector. Depending upon the detection range of the low SWaP sensor, the UAS pilot may or may not have time to coordinate with air traffic controllers (ATCos) prior to performing the DAA maneuver. ATCo’s sector performance and subjective ratings of acceptability were obtained in four conditions that varied in UAS-ATCo coordination (all or none) prior to the DAA maneuver and workload (higher or lower). For performance, ATCos committed more losses of separation in high than low workload conditions. They also had to make more flight plan changes to manage the UAS when the UAS pilot did not coordinate DAA maneuvers compared to when they did coordinate the maneuvers prior to execution. Although the ATCos found the DAA procedures used by the UAS in the study to be acceptable, most preferred the UAS pilot to coordinate their DAA maneuvers with ATCos prior to executing them.

Altitude Effects on the Performance of Small Radial Turbomachinery: Part I — Compressor Impellers

2016 ◽  
Author(s):  
Dries Verstraete ◽  
Kjersti Lunnan
Keyword(s):  
Reynolds Number ◽  
Performance Analysis ◽  
Rotational Speed ◽  
Pressure Ratio ◽  
Unmanned Aircraft ◽  
Good Match ◽  
One Dimensional ◽  
Design Changes ◽  
Current Article ◽  
The Impact

Small unmanned aircraft are currently limited to flight ceilings below 20,000 ft due to the lack of an appropriate propulsion system. One of the most critical technological hurdles for an increased flight ceiling of small platforms is the impact of reduced Reynolds number conditions at altitude on the performance of small radial turbomachinery. The current article investigates the influence of Reynolds number on the efficiency and pressure ratio of two small centrifugal compressor impellers using a one-dimensional meanline performance analysis code. The results show that the efficiency and pressure ratio of the 60 mm baseline compressor at the design rotational speed drops with 6–9% from sea-level to 70,000 ft. The impact on the smaller 20 mm compressor is slightly more pronounced and amounts to 6–10%. Off-design changes at low rotational speeds are significantly higher and can amount to up to 15%. Whereas existing correlations show a good match for the efficiency drop at the design rotational speed, they fail to predict efficiency changes with rotational speed. A modified version is therefore proposed.

Methods to Reduce Traffic Speed in High-Pedestrian Rural Areas

2003 ◽  
Vol 1828 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Ali Kamyab ◽  
Steve Andrle ◽  
Dennis Kroeger ◽  
David S. Heyer
Keyword(s):  
Rural Areas ◽  
Speed Limit ◽  
Single Word ◽  
Short Term ◽  
Speed Reduction ◽  
Traffic Calming ◽  
Reduction Strategies ◽  
Long Term Impact ◽  
The Impact

Many Minnesota counties are faced with the problem of high vehicle speeds through towns or resort areas that have significant pedestrian traffic. The impact of speed reduction strategies in high-pedestrian areas in rural counties of Minnesota was investigated. Speed data were collected at two selected study sites under their existing conditions ("no-treatment" or "before" condition) and after the proposed speed reduction strategies were installed. Second "after" data conditions were collected to study the short-term and long-term impact of the implemented strategies. The traffic-calming techniques employed at the Twin Lakes site consisted of removable pedestrian islands and pedestrian crossing signs. A dynamic variable message sign that sent a single-word message ("Slow") to motorists traveling over the speed limit was installed at the Bemidji site. The research study shows that the traffic-calming strategy deployed in Twin Lakes was effective in significantly reducing the mean speed and improving speed limit compliance in both the short term and long term. Despite proven effectiveness, the deployed speed reduction treatment in Bemidji Lake failed to lower the speed at the study site. The single-word message on the sign and the location of the sign, as well as a lack of initial enforcement, were the primary reasons for such failure.

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