scholarly journals The Katwijk beach planetary rover dataset

2017 ◽  
Vol 37 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Robert A Hewitt ◽  
Evangelos Boukas ◽  
Martin Azkarate ◽  
Marco Pagnamenta ◽  
Joshua A Marshall ◽  
...  
Keyword(s):  
Satellite System ◽  
Entire Area ◽  
Mapping Algorithms ◽  
Planetary Rover ◽  
Localization And Mapping ◽  
Stereo Cameras ◽  
Landing Sites ◽  
Aerial Vehicle ◽  
Global Navigation Satellite ◽  
Rock Size

This paper describes a dataset collected along a 1 km section of beach near Katwijk, The Netherlands, which was populated with a collection of artificial rocks of varying sizes to emulate known rock size densities at current and potential Mars landing sites. First, a fixed-wing unmanned aerial vehicle collected georeferenced images of the entire area. Then, the beach was traversed by a rocker-bogie-style rover equipped with a suite of sensors that are envisioned for use in future planetary rover missions. These sensors, configured so as to emulate the ExoMars rover, include stereo cameras, and time-of-flight and scanning light-detection-and-ranging sensors. This dataset will be of interest to researchers developing localization and mapping algorithms for vehicles traveling over natural and unstructured terrain in environments that do not have access to the global navigation satellite system, and where only previously taken satellite or aerial imagery is available.

scholarly journals An Image-Based Real-Time Georeferencing Scheme for a UAV Based on a New Angular Parametrization

2020 ◽  
Vol 12 (19) ◽  
pp. 3185
Author(s):  
Ehsan Khoramshahi ◽  
Raquel A. Oliveira ◽  
Niko Koivumäki ◽  
Eija Honkavaara
Keyword(s):  
Real Time ◽  
Satellite System ◽  
Local Networks ◽  
Ground Control Points ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Aerial Vehicle ◽  
Gimbal Lock ◽  
Global Navigation Satellite ◽  
Pyramid Matching

Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.

scholarly journals Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

2021 ◽  
pp. 1-13
Author(s):  
Jonghyuk Kim ◽  
Jose Guivant ◽  
Martin L. Sollie ◽  
Torleiv H. Bryne ◽  
Tor Arne Johansen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Computational Cost ◽  
Satellite System ◽  
Measurement Unit ◽  
Electronic Systems ◽  
Computationally Efficient ◽  
Global Correlation ◽  
Aerial Vehicle ◽  
Correlation Information ◽  
Global Navigation Satellite

Abstract This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

scholarly journals Methodology for the Correction of the Spatial Orientation Angles of the Unmanned Aerial Vehicle Using Real Time GNSS, a Shoreline Image and an Electronic Navigational Chart

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2810
Author(s):  
Krzysztof Naus ◽  
Piotr Szymak ◽  
Paweł Piskur ◽  
Maciej Niedziela ◽  
Aleksander Nowak
Keyword(s):  
Real Time ◽  
Spatial Orientation ◽  
Orientation Angle ◽  
Satellite System ◽  
Angle Measurement ◽  
Angle Correction ◽  
Lidar Measurements ◽  
Aerial Vehicle ◽  
Marine Applications ◽  
Global Navigation Satellite

Undoubtedly, Low-Altitude Unmanned Aerial Vehicles (UAVs) are becoming more common in marine applications. Equipped with a Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) receiver for highly accurate positioning, they perform camera and Light Detection and Ranging (LiDAR) measurements. Unfortunately, these measurements may still be subject to large errors-mainly due to the inaccuracy of measurement of the optical axis of the camera or LiDAR sensor. Usually, UAVs use a small and light Inertial Navigation System (INS) with an angle measurement error of up to 0.5∘ (RMSE). The methodology for spatial orientation angle correction presented in the article allows the reduction of this error even to the level of 0.01∘ (RMSE). It can be successfully used in coastal and port waters. To determine the corrections, only the Electronic Navigational Chart (ENC) and an image of the coastline are needed.

scholarly journals Aerial and Ground Robot Collaboration for Autonomous Mapping in Search and Rescue Missions

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 79
Author(s):  
Dimitrios Chatziparaschis ◽  
Michail G. Lagoudakis ◽  
Panagiotis Partsinevelos
Keyword(s):  
Humanoid Robot ◽  
Satellite System ◽  
Search And Rescue ◽  
Localization Algorithm ◽  
Localization And Mapping ◽  
Planning Algorithm ◽  
Crisis Scenarios ◽  
Trained Neural Network ◽  
Unknown Area ◽  
Global Navigation Satellite

Humanitarian Crisis scenarios typically require immediate rescue intervention. In many cases, the conditions at a scene may be prohibitive for human rescuers to provide instant aid, because of hazardous, unexpected, and human threatening situations. These scenarios are ideal for autonomous mobile robot systems to assist in searching and even rescuing individuals. In this study, we present a synchronous ground-aerial robot collaboration approach, under which an Unmanned Aerial Vehicle (UAV) and a humanoid robot solve a Search and Rescue scenario locally, without the aid of a commonly used Global Navigation Satellite System (GNSS). Specifically, the UAV uses a combination of Simultaneous Localization and Mapping and OctoMap approaches to extract a 2.5D occupancy grid map of the unknown area in relation to the humanoid robot. The humanoid robot receives a goal position in the created map and executes a path planning algorithm in order to estimate the FootStep navigation trajectory for reaching the goal. As the humanoid robot navigates, it localizes itself in the map while using an adaptive Monte-Carlo Localization algorithm by combining local odometry data with sensor observations from the UAV. Finally, the humanoid robot performs visual human body detection while using camera data through a Darknet pre-trained neural network. The proposed robot collaboration scheme has been tested under a proof of concept setting in an exterior GNSS-denied environment.

scholarly journals THERMAL IR IMAGING: IMAGE QUALITY AND ORTHOPHOTO GENERATION

2018 ◽  
Vol XLII-1 ◽  
pp. 413-420 ◽  
Author(s):  
A. Sledz ◽  
J. Unger ◽  
C. Heipke
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Automatic Generation ◽  
Satellite System ◽  
Night Time ◽  
World Coordinate System ◽  
Image Capturing ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> This paper deals with two aspects of photogrammetric processing of thermal images: image quality and 3D reconstruction quality. The first aspect of the paper relates to the influence of day light on Thermal InfraRed (TIR) images captured by an Unmanned Aerial Vehicle (UAV). Environmental factors such as ambient temperature and lack of sun light affect TIR image quality. We acquire image sequences of the same object during day and night and compare the generated orthophotos according to different metrics like contrast and signal-to-noise ratio (SNR). Our experiments show that performing TIR image acquisition during night time provides a better thermal contrast, regardless of whether we compute contrast over the whole image or over small patches. The second aspect investigated in this work is the potential of using TIR images for photogrammetric tasks such as the automatic generation of Digital Surface Models (DSM) and orthophotos. Due to the low geometrical resolution of a TIR camera and the low image quality in terms of contrast and noise compared to RGB images, the TIR DSM suffers from reconstruction errors and an orthophoto generated using the TIR DSM and TIR images is visibly influenced by those errors. We therefore include measurements of the UAVs positions during image capturing provided by a Global Navigation Satellite System (GNSS) receiver to retrieve position and orientation of TIR and RGB images in the same world coordinate system. To generate an orthophoto from TIR images, they are projected onto the DSM reconstructed from RGB images. This procedure leads to a TIR orthophoto of much higher quality in terms of geometrical correctness.</p>

scholarly journals UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

2019 ◽  
Vol IV-2/W5 ◽  
pp. 405-412 ◽  
Author(s):  
A. Mayr ◽  
M. Bremer ◽  
M. Rutzinger ◽  
C. Geitner
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Test Site ◽  
Satellite System ◽  
Alpine Grassland ◽  
Proof Of Concept ◽  
Aerial Vehicle ◽  
Geomorphological Changes ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (<i>bare earth</i>, <i>grassland</i>, <i>trees</i>), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&amp;thinsp;m<sup>3</sup> is estimated for the test site (48&amp;thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.</p>

scholarly journals Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment

2020 ◽  
Vol 12 (10) ◽  
pp. 1564 ◽  
Author(s):  
Kai-Wei Chiang ◽  
Guang-Je Tsai ◽  
Yu-Hua Li ◽  
You Li ◽  
Naser El-Sheimy
Keyword(s):  
Urban Areas ◽  
Satellite System ◽  
Multisensor Fusion ◽  
Automated Driving ◽  
Integrity Assessment ◽  
Localization And Mapping ◽  
Self Driving Cars ◽  
Global Navigation Satellite ◽  
3D Lidar ◽  
Ins Aiding

Automated driving has made considerable progress recently. The multisensor fusion system is a game changer in making self-driving cars possible. In the near future, multisensor fusion will be necessary to meet the high accuracy needs of automated driving systems. This paper proposes a multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM). The proposed fusion structure enhances the conventional INS/GNSS/odometer by compensating for individual drawbacks such as INS-drift and error-contaminated GNSS. First, a highly integrated INS-aiding LiDAR-SLAM is presented to improve the performance and increase the robustness to adjust to varied environments using the reliable initial values from the INS. Second, the proposed fault detection exclusion (FDE) contributes SLAM to eliminate the failure solutions such as local solution or the divergence of algorithm. Third, the SLAM position velocity acceleration (PVA) model is used to deal with the high dynamic movement. Finally, an integrity assessment benefits the central fusion filter to avoid failure measurements into the update process based on the information from INS-aiding SLAM, which increases the reliability and accuracy. Consequently, our proposed multisensor design can deal with various situations such as long-term GNSS outage, deep urban areas, and highways. The results show that the proposed method can achieve an accuracy of under 1 meter in challenging scenarios, which has the potential to contribute the autonomous system.

scholarly journals Utilizing Airborne LiDAR and UAV Photogrammetry Techniques in Local Geoid Model Determination and Validation

2020 ◽  
Vol 9 (9) ◽  
pp. 528 ◽  
Author(s):  
Serdar Erol ◽  
Emrah Özögel ◽  
Ramazan Alper Kuçak ◽  
Bihter Erol
Keyword(s):  
Point Clouds ◽  
Satellite System ◽  
Airborne Lidar ◽  
Geoid Model ◽  
Digital Terrain ◽  
Novel Approach ◽  
Aerial Vehicle ◽  
Global Navigation Satellite ◽  
Regional Gravimetric Geoid ◽  
Local Geoid

This investigation evaluates the performance of digital terrain models (DTMs) generated in different vertical datums by aerial LiDAR and unmanned aerial vehicle (UAV) photogrammetry techniques, for the determination and validation of local geoid models. Many engineering projects require the point heights referring to a physical surface, i.e., geoid, rather than an ellipsoid. When a high-accuracy local geoid model is available in the study area, the physical heights are practically obtained with the transformation of global navigation satellite system (GNSS) ellipsoidal heights of the points. Besides the commonly used geodetic methods, this study introduces a novel approach for the determination and validation of the local geoid surface models using photogrammetry. The numeric tests were carried out in the Bergama region, in the west of Turkey. Using direct georeferenced airborne LiDAR and indirect georeferenced UAV photogrammetry-derived point clouds, DTMs were generated in ellipsoidal and geoidal vertical datums, respectively. After this, the local geoid models were calculated as differences between the generated DTMs. Generated local geoid models in the grid and pointwise formats were tested and compared with the regional gravimetric geoid model (TG03) and a high-resolution global geoid model (EIGEN6C4), respectively. In conclusion, the applied approach provided sufficient performance for modeling and validating the geoid heights with centimeter-level accuracy.

scholarly journals Hybrid 3D Models: When Geomatics Innovations Meet Extensive Built Heritage Complexes

2019 ◽  
Vol 8 (3) ◽  
pp. 124 ◽  
Author(s):  
Filiberto Chiabrando ◽  
Giulia Sammartano ◽  
Antonia Spanò ◽  
Alessandra Spreafico
Keyword(s):  
Three Dimensional ◽  
Laser Scanner ◽  
3D Models ◽  
Satellite System ◽  
3D Data ◽  
Mapping System ◽  
Aerial Vehicle ◽  
Mobile Mapping System ◽  
Global Navigation Satellite ◽  
Topographic Surveys

This article proposes the use of a multiscale and multisensor approach to collect and model three-dimensional (3D) data concerning wide and complex areas to obtain a variety of metric information in the same 3D archive, which is based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors’ data, scales, and resolutions is promising, and it could be useful in the generation of a model that could be defined as a hybrid. The correct geometry, accuracy, radiometry, and weight of the data models are hereby evaluated when comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), and terrestrial photogrammetry, while using Total Station (TS) and Global Navigation Satellite System (GNSS) for topographic surveys. The entire analysis underlines the potentiality of the integration and fusion of different solutions and it is a crucial part of the ‘Torino 1911’ project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

KONTRIBUSI GLOBAL NAVIGATION SATELLITE SYSTEM PADA PENENTUAN TITIK KONTROL UDARA DI METODE FOTOGRAMETRI UNMANNED AERIAL VEHICLE UNTUK PEMETAAN SKALA BESAR DI INDONESIA

2021 ◽  
pp. 867
Author(s):  
Irwan Gumilar ◽  
Deni Suwardhi ◽  
Irfan Budaya ◽  
Brian Bramanto ◽  
Kamal Nur Fauzan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Mean Square ◽  
Post Processing ◽  
Check Points ◽  
Aerial Vehicle ◽  
Global Navigation ◽  
Global Navigation Satellite

Indonesia saat ini sedang melakukan pemetaan skala besar secara masif. Salah satu metode yang digunakan pada pemetaan skala besar tersebut adalah dengan menggunakan teknik fotogrametri berbasiskan Unmanned Aerial Vehicle (UAV). Saat ini, metode penentuan titik kontrol udara dengan menggunakan Global Navigation Satellite System (GNSS) banyak dilakukan untuk memimalisir jumlah titik kontrol tanah tanpa mengurangi kualitas dari produk fotogrameteri yang dihasilkan. Penelitian ini bertujuan untuk menganalisa kontribusi sistem GNSS pada penentuan titik kontrol udara untuk metode fotogrametri berbasiskan UAV. Pengukuran GNSS frekuensi ganda pada sistem UAV di wilayah Jatinangor, Bandung dan Panglipuran Bali digunakan pada penelitian ini. Panjang baseline antara titik kontrol dan rover berkisar antara 350 hingga 900 m. Penentuan posisi titik kontrol udara berbasiskan GNSS menggunakan metode Post Processing Kinematic (PPK) dengan teknik pemecahan ambiguitas fase LAMBDA Fix and Hold. Pengolahan data GNSS dilakukan dengan menggunakan beberapa kombinasi frekuensi dan sistem GNSS. Evaluasi ketelitian hasil perataan berkas menggunakan titik kontrol udara pada setiap kombinasi frekuensi dan sistem GNSS dilakukan dengan memperhatikan nilai Root Mean Square Error (RMSE) pada 20 titik cek tanah atau Independent Check Points (ICP). Berdasarkan hasil tersebut, kombinasi gelombang L1 dan L2 menggunakan sistem GPS dan BeiDou idealnya digunakan untuk pemetaan skala besar menggunakan fotogrametri UAV. Selain itu, kombinasi data GPS dan Beidou frekuensi ganda memiliki tingkat ketelitian titik kontrol udara yang terbaik dibandingkan kombinasi yang lainnya. Selain itu, kombinasi GPS dan BeiDou menggunakan hanya gelombang L1 memiliki tingkat ketelitian yang sama dibandingkan dengan GPS menggunakan gelombang L1 dan L2.

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