N. meky

Bio: N. meky is an academic researcher from Mansoura University. The author has contributed to research in topics: Color histogram & Feature detection (computer vision). The author has an hindex of 1, co-authored 1 publications receiving 82 citations.

A Survey on Odometry for Autonomous Navigation Systems

Abstract: The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, self-contained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

A Comparative Evaluation of Well-known Feature Detectors and Descriptors

Abstract: Comparison of feature detectors and descriptors and assessing their performance is very important in computer vision. In this study, we evaluate the performance of seven combination of well-known detectors and descriptors which are SIFT with SIFT, SURF with SURF, MSER with SIFT, BRISK with FREAK, BRISK with BRISK, ORB with ORB and FAST with BRIEF. The popular Oxford dataset is used in test stage. To compare the performance of each combination objectively, the effects of JPEG compression, zoom and rotation, blur, viewpoint and illumination variation have investigated in terms of precision and recall values. Upon inspecting the obtained results, it is observed that the combination of ORB with ORB and MSER with SIFT can be preferable almost in all possible situations when the precision and recall results are considered. Moreover, the speed of FAST with BRIEF is superior to others.

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

Abstract: Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20–0.30 m, but decreases to 0.06–0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.