Image matching applied to autonomous navigation of unmanned aerial vehicles

Silva, C. A. O.; Goltz, G. A. M.; Shiguemori, Elcio Hideiti; Castro, Carlos Henrique Silva de; Velho, Haroldo Fraga de Campos; Braga, Antônio de Pádua

doi:10.1504/ijhpsa.2016.081751

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Many solutions have been proposed for autonomous aerial navigation in GNSS-denied scenarios. One branch of research is to use Computer Vision techniques that allow the UAV to locate itself by means of template matching [24], landmark recognition [25], odometry [26], among others [27][28][29]. Those approaches are usually validated using standard optical RGB cameras.…”

Section: Introductionmentioning

confidence: 99%

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Salles¹,

Velho

Shiguemori³

2022

Remote Sensing

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In this paper we post-process and evaluate the position estimation of pairs of template windows and geo-referenced images generated from LiDAR cloud point data using the Normalized Cross-Correlation (NCC) method. We created intensity, surface and terrain pairs of images for use with template matching, with 5 m pixel spacing, through binning. We evaluated square and circular binning approaches, without filtering the original data. Template matching achieved approximately 7 m root mean square error (RMSE) on intensity and surface templates on the respective geo-referenced images, while on terrain templates it had many mismatches due to insufficient terrain features over the assumed flight transect. Analysis of NCC showed the possibility of rejecting bad matches of intensity and surface templates, but terrain templates required an additional criteria of flatness for rejection. The combined NCC of intensity, surface and terrain proved stable for rejection of bad matches and had the lowest RMSE. Filtering outliers from surface images changed very little the accuracy of the matches, but greatly improved correlation values, indicating that the forest canopy might have the best features for geo-localization with template matching. Position estimation is essential for autonomous navigation of aerial vehicles and the these experiments with LiDAR data show potential for localization over densely forested regions where methods using optical camera data may fail to acquire distinguishable features.

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Section: Introductionmentioning

confidence: 99%

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Salles¹,

Velho

Shiguemori³

2022

Remote Sensing

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Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Batista,

Öberg,

Saotome

et al. 2024

Journal of Electronic Science and Technology

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Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

2019

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Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

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Image matching applied to autonomous navigation of unmanned aerial vehicles

Cited by 3 publications

References 10 publications

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

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