Mattie N. Milner, S. Rice, S. Winter, Emily C. Anania
As unmanned aerial systems grow in popularity, police agencies are using this technology to provide aerial support for officers; however, public opinion could affect the success of this technological collaboration. Using social identity theory, researchers may be able to predict people’s support for various government projects. In a series of studies, participants were presented with a brief description of a proposal for using police drones to monitor political protests. Additional information was provided about the type of protest and type of person attending the protest. In general, conservatives were more supportive of police drones monitoring protests compared to liberals. However, this support was moderated by the type of participant and the type of protest; that is, support dropped when a participant believed that the protest supported their own political party beliefs. The current study provides a foundation for understanding what factors affect the public’s support of police incorporating drones into their daily workforce in the US.
{"title":"The effect of political affiliation on support for police drone monitoring in the United States","authors":"Mattie N. Milner, S. Rice, S. Winter, Emily C. Anania","doi":"10.1139/JUVS-2018-0026","DOIUrl":"https://doi.org/10.1139/JUVS-2018-0026","url":null,"abstract":"As unmanned aerial systems grow in popularity, police agencies are using this technology to provide aerial support for officers; however, public opinion could affect the success of this technological collaboration. Using social identity theory, researchers may be able to predict people’s support for various government projects. In a series of studies, participants were presented with a brief description of a proposal for using police drones to monitor political protests. Additional information was provided about the type of protest and type of person attending the protest. In general, conservatives were more supportive of police drones monitoring protests compared to liberals. However, this support was moderated by the type of participant and the type of protest; that is, support dropped when a participant believed that the protest supported their own political party beliefs. The current study provides a foundation for understanding what factors affect the public’s support of police incorporating drones into their daily workforce in the US.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2018-0026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46356422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Coe, C. Dunbar, Keunta Epps, Joseph Hagensee, A. Moore
In this work, a four-person student team was given the challenge of designing, analysing, constructing, and testing a low-altitude unmanned aerial vehicle (UAV) prototype, which could meet or exceed a set of predefined performance requirements including range, altitude, time of flight, and load-carrying capability. In addition, the team was tasked with having their final design be composed of at least 70% sustainable material by volume. The final prototype took the form of a quadcopter with an airframe 3D printed from a plant-based bioplastic. This prototype was able to meet or exceed three of the four project performance targets, with time of flight being the lone failure. Besides serving as a proof-of-concept prototype of a functioning bioplastic-based UAV, this project is also a demonstration of 3D printing as an enabling technology that can allow even small design teams to realize complex geometries, enjoy enhanced design flexibility, and achieve high levels of UAV functionality with relatively limited resources. Finally, a discussion of important material parameters of 3D printed UAVs is presented.
{"title":"A low-altitude unmanned aerial vehicle (UAV) created using 3D-printed bioplastic","authors":"J. Coe, C. Dunbar, Keunta Epps, Joseph Hagensee, A. Moore","doi":"10.1139/JUVS-2017-0023","DOIUrl":"https://doi.org/10.1139/JUVS-2017-0023","url":null,"abstract":"In this work, a four-person student team was given the challenge of designing, analysing, constructing, and testing a low-altitude unmanned aerial vehicle (UAV) prototype, which could meet or exceed a set of predefined performance requirements including range, altitude, time of flight, and load-carrying capability. In addition, the team was tasked with having their final design be composed of at least 70% sustainable material by volume. The final prototype took the form of a quadcopter with an airframe 3D printed from a plant-based bioplastic. This prototype was able to meet or exceed three of the four project performance targets, with time of flight being the lone failure. Besides serving as a proof-of-concept prototype of a functioning bioplastic-based UAV, this project is also a demonstration of 3D printing as an enabling technology that can allow even small design teams to realize complex geometries, enjoy enhanced design flexibility, and achieve high levels of UAV functionality with relatively limited resources. Finally, a discussion of important material parameters of 3D printed UAVs is presented.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2017-0023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45942030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Winter, S. Rice, Rian Mehta, Nathan W. Walters, Matthew Pierce, Emily C. Anania, Mattie N. Milner, N. Rao
The purpose of this study was to examine a person’s willingness to ride (WTR) in an autonomous bus. Across two studies, we presented participants with hypothetical scenarios about riding in a driverless city or inter-city bus. We manipulated who was onboard the bus (participant, romantic partner, or child), the location of the bus (seven different countries), and the type of driver (human or driverless). In Study 1, participants were less willing to ride a driverless city bus compared to one driven by a human driver. In Study 2, participants’ WTR scores were influenced by participant gender, the person on board, and location, with scores dropping dramatically when the bus was located outside of the USA, or when a child was on board. The current data suggest that Americans are not entirely ready for driverless buses, mainly when someone they care about is on board, or the bus is located outside the USA.
{"title":"Do Americans differ in their willingness to ride in a driverless bus?","authors":"S. Winter, S. Rice, Rian Mehta, Nathan W. Walters, Matthew Pierce, Emily C. Anania, Mattie N. Milner, N. Rao","doi":"10.1139/JUVS-2018-0020","DOIUrl":"https://doi.org/10.1139/JUVS-2018-0020","url":null,"abstract":"The purpose of this study was to examine a person’s willingness to ride (WTR) in an autonomous bus. Across two studies, we presented participants with hypothetical scenarios about riding in a driverless city or inter-city bus. We manipulated who was onboard the bus (participant, romantic partner, or child), the location of the bus (seven different countries), and the type of driver (human or driverless). In Study 1, participants were less willing to ride a driverless city bus compared to one driven by a human driver. In Study 2, participants’ WTR scores were influenced by participant gender, the person on board, and location, with scores dropping dramatically when the bus was located outside of the USA, or when a child was on board. The current data suggest that Americans are not entirely ready for driverless buses, mainly when someone they care about is on board, or the bus is located outside the USA.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2018-0020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41924730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the recent proliferation of unmanned aerial vehicles for geophysical surveying, a novel opportunity exists to develop unmanned ground vehicles in parallel. This contribution features a study to integrate the Husky A200 robotic development platform with a GSMP 35U magnetometer that has recently been developed for the unmanned aerial vehicle market. Methods to identify and reduce the impact of magnetically noisy components on the unmanned ground vehicle platforms are discussed. The noise generated by the platform in laboratory and gentle field conditions, estimated using the fourth difference method for a magnetometer–vehicle separation distance of 121 cm and rotation of the chassis wheels at full speed (1 m/s), is ±1.97 nT. The integrated unmanned ground vehicle was used to conduct two robotic magnetic surveys to map cultural targets and natural variations of the magnetic field. In realistic field conditions, at a full speed of 1 m/s, the unmanned ground vehicle measured total magnetic intensity over a range of 1730 nT at 0.1 m spatial resolution with a productivity of 2651 line metres per hour.
{"title":"Magnetic surveying with an unmanned ground vehicle","authors":"A. Hay, C. Samson, L. Tuck, A. Ellery","doi":"10.1139/JUVS-2018-0013","DOIUrl":"https://doi.org/10.1139/JUVS-2018-0013","url":null,"abstract":"With the recent proliferation of unmanned aerial vehicles for geophysical surveying, a novel opportunity exists to develop unmanned ground vehicles in parallel. This contribution features a study to integrate the Husky A200 robotic development platform with a GSMP 35U magnetometer that has recently been developed for the unmanned aerial vehicle market. Methods to identify and reduce the impact of magnetically noisy components on the unmanned ground vehicle platforms are discussed. The noise generated by the platform in laboratory and gentle field conditions, estimated using the fourth difference method for a magnetometer–vehicle separation distance of 121 cm and rotation of the chassis wheels at full speed (1 m/s), is ±1.97 nT. The integrated unmanned ground vehicle was used to conduct two robotic magnetic surveys to map cultural targets and natural variations of the magnetic field. In realistic field conditions, at a full speed of 1 m/s, the unmanned ground vehicle measured total magnetic intensity over a range of 1730 nT at 0.1 m spatial resolution with a productivity of 2651 line metres per hour.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2018-0013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46168112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alton Yeung, Goetz Bramesfeld, Joon Chung, S. Foster
A small unmanned aerial vehicle (SUAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes below 500 m. These gusts have significant impact on the flight characteristics and performance of SUAVs. The SUAV carried an advanced air-data system that includes a five-hole probe, which was adapted for this specific application. In several flight tests the entire test system was qualified and gust data were recorded. The subsequent experimentally derived gust data were post-processed and compared with turbulence spectra of the MIL-HDBK-1797 von Kármán turbulence model. On the day of the flight test, the experimental results did not fully match the prediction of the von Kármán model. Meanwhile, the wind measuring apparatus were proven to be able to measure gust during flight. Therefore, a broader sampling will be required to generalize the gust measurements and be compared with the existing models.
开发了一种小型无人机(SUAV)，其具体目标是探索500米以下低空的大气阵风。这些阵风对无人机的飞行特性和性能有重要影响。该无人机携带了先进的空气数据系统，其中包括一个五孔探头，适用于这种特殊应用。在几次飞行测试中，整个测试系统都是合格的，并记录了阵风数据。随后实验得到的阵风数据进行了后处理，并与MIL-HDBK-1797 von Kármán湍流模型的湍流谱进行了比较。在飞行试验当天，实验结果与von Kármán模型的预测并不完全吻合。同时，风力测量仪器已被证明能够测量飞行过程中的阵风。因此，将需要更广泛的采样来推广阵风测量并与现有模型进行比较。
{"title":"Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV","authors":"Alton Yeung, Goetz Bramesfeld, Joon Chung, S. Foster","doi":"10.1139/JUVS-2017-0029","DOIUrl":"https://doi.org/10.1139/JUVS-2017-0029","url":null,"abstract":"A small unmanned aerial vehicle (SUAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes below 500 m. These gusts have significant impact on the flight characteristics and performance of SUAVs. The SUAV carried an advanced air-data system that includes a five-hole probe, which was adapted for this specific application. In several flight tests the entire test system was qualified and gust data were recorded. The subsequent experimentally derived gust data were post-processed and compared with turbulence spectra of the MIL-HDBK-1797 von Kármán turbulence model. On the day of the flight test, the experimental results did not fully match the prediction of the von Kármán model. Meanwhile, the wind measuring apparatus were proven to be able to measure gust during flight. Therefore, a broader sampling will be required to generalize the gust measurements and be compared with the existing models.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2017-0029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44932154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We examined the horizontal and vertical accuracy of LiDAR data acquired from an unmanned aerial vehicle (UAV) at a field site with six vegetation types: coniferous trees, deciduous trees, short grass (0–0.3 m height), tall grass (>0.3 m height), short shrubs (0–1 m height), and tall shrubs (>1 m height). The objective was to assess positional accuracy of the ground surface in the context of digital mapping standards, and to determine how different vegetation types affect vertical accuracy. The data were acquired from a single-rotor vertical takeoff and landing UAV equipped with a Riegl VUX-1UAV laser scanner, KVH Industries 1750 IMU, and dual NovAtel GNSS receivers. Reference measurements of ground surface elevation were acquired with conventional field surveying techniques. Accuracy was evaluated using methods in the 2015 American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data. Results show that horizontal accuracy and vegetated vertical accuracy at the 95% confidence level were 0.05 and 0.24 m, respectively. Median vertical errors significantly differed among 10 of 15 vegetation type pairs, highlighting the need to account for variations of vegetation structure. According to the 2015 ASPRS standards, the reported errors fulfill the requirements for mapping at the 2 and 8 cm horizontal and vertical class levels, respectively.
{"title":"UAV–LiDAR accuracy in vegetated terrain","authors":"Maja Kucharczyk, C. Hugenholtz, Xueyang Zou","doi":"10.1139/JUVS-2017-0030","DOIUrl":"https://doi.org/10.1139/JUVS-2017-0030","url":null,"abstract":"We examined the horizontal and vertical accuracy of LiDAR data acquired from an unmanned aerial vehicle (UAV) at a field site with six vegetation types: coniferous trees, deciduous trees, short grass (0–0.3 m height), tall grass (>0.3 m height), short shrubs (0–1 m height), and tall shrubs (>1 m height). The objective was to assess positional accuracy of the ground surface in the context of digital mapping standards, and to determine how different vegetation types affect vertical accuracy. The data were acquired from a single-rotor vertical takeoff and landing UAV equipped with a Riegl VUX-1UAV laser scanner, KVH Industries 1750 IMU, and dual NovAtel GNSS receivers. Reference measurements of ground surface elevation were acquired with conventional field surveying techniques. Accuracy was evaluated using methods in the 2015 American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data. Results show that horizontal accuracy and vegetated vertical accuracy at the 95% confidence level were 0.05 and 0.24 m, respectively. Median vertical errors significantly differed among 10 of 15 vegetation type pairs, highlighting the need to account for variations of vegetation structure. According to the 2015 ASPRS standards, the reported errors fulfill the requirements for mapping at the 2 and 8 cm horizontal and vertical class levels, respectively.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2017-0030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46923029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.
{"title":"Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles","authors":"S. Franklin","doi":"10.1139/JUVS-2017-0022","DOIUrl":"https://doi.org/10.1139/JUVS-2017-0022","url":null,"abstract":"Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2017-0022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44771826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Tuck, C. Samson, J. Laliberté, M. Wells, F. Belanger
One of the barriers preventing unmanned aircraft systems (UASs) from having a larger presence in the geophysical magnetic surveying industry is the magnetic interference generated by the UAS and its impact on the quality of the recorded data. Detailed characterization of interference effects is therefore needed before remedial solutions can be proposed. A method for characterizing magnetic interference is demonstrated for a 21 kg, 3.7 m wingspan, 6 kW electric fixed-wing UAS purposely built for magnetic surveying. It involves mapping the spatial variations of the total magnetic intensity resulting from the interference sources on the UAS. Dynamic tests showed that the motor should be engaged and the aircraft control surfaces levelled prior to mapping. Experimental results reveal that the two strongest sources of magnetic interference are the cables connecting the motor to the batteries, and the servos. Combining three factors to assess the level of magnetic interference — the total magnetic intensity, 4th difference and vertical magnetic gradient — an index overlay shows that the magnetic sensor(s) should be located at least 50 cm away from the wingtips or tail to ensure an interference level of <2 nT, a 4th difference of <0.05 nT, and a gradient of <10 nT/m.
{"title":"Magnetic interference testing method for an electric fixed-wing unmanned aircraft system (UAS)","authors":"L. Tuck, C. Samson, J. Laliberté, M. Wells, F. Belanger","doi":"10.1139/JUVS-2018-0006","DOIUrl":"https://doi.org/10.1139/JUVS-2018-0006","url":null,"abstract":"One of the barriers preventing unmanned aircraft systems (UASs) from having a larger presence in the geophysical magnetic surveying industry is the magnetic interference generated by the UAS and its impact on the quality of the recorded data. Detailed characterization of interference effects is therefore needed before remedial solutions can be proposed. A method for characterizing magnetic interference is demonstrated for a 21 kg, 3.7 m wingspan, 6 kW electric fixed-wing UAS purposely built for magnetic surveying. It involves mapping the spatial variations of the total magnetic intensity resulting from the interference sources on the UAS. Dynamic tests showed that the motor should be engaged and the aircraft control surfaces levelled prior to mapping. Experimental results reveal that the two strongest sources of magnetic interference are the cables connecting the motor to the batteries, and the servos. Combining three factors to assess the level of magnetic interference — the total magnetic intensity, 4th difference and vertical magnetic gradient — an index overlay shows that the magnetic sensor(s) should be located at least 50 cm away from the wingtips or tail to ensure an interference level of <2 nT, a 4th difference of <0.05 nT, and a gradient of <10 nT/m.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2018-0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46041467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned aerial vehicles (UAVs) have been developed to be used in complex environments. Continuity of a UAV operation when GPS is degraded or denied is crucial in many applications, such as flying near high buildings and trees, or flying outdoor-to-indoor. In this paper, an algorithm for 3D-localization during transition between indoor and outdoor environments for a UAV is presented. Localization inputs are based on information from GPS, inertial measurement unit, monocular camera, and optical flow sensor. Information is carefully selected using GPS quality indicator method corresponding to the operating environment. After that, a smoothing offset approach is employed to smooth the position estimation. The selected sensors’ data are filtered by indirect extended Kalman filter for localization and extrinsic sensor calibration in real time. Results show a seamless offset convergence of UAV localization for indoor–outdoor transition. Moreover, the proposed method of decision-making to cut off GPS measurement even when it experiences poor signal quality can still outperform conventional GPS-based cutoff method in terms of response time.
{"title":"A method for UAV multi-sensor fusion 3D-localization under degraded or denied GPS situation","authors":"Thanabadee Bulunseechart, P. Smithmaitrie","doi":"10.1139/JUVS-2018-0007","DOIUrl":"https://doi.org/10.1139/JUVS-2018-0007","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have been developed to be used in complex environments. Continuity of a UAV operation when GPS is degraded or denied is crucial in many applications, such as flying near high buildings and trees, or flying outdoor-to-indoor. In this paper, an algorithm for 3D-localization during transition between indoor and outdoor environments for a UAV is presented. Localization inputs are based on information from GPS, inertial measurement unit, monocular camera, and optical flow sensor. Information is carefully selected using GPS quality indicator method corresponding to the operating environment. After that, a smoothing offset approach is employed to smooth the position estimation. The selected sensors’ data are filtered by indirect extended Kalman filter for localization and extrinsic sensor calibration in real time. Results show a seamless offset convergence of UAV localization for indoor–outdoor transition. Moreover, the proposed method of decision-making to cut off GPS measurement even when it experiences poor signal quality can still outperform conventional GPS-based cutoff method in terms of response time.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2018-0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44566889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Ferguson, R. Angliss, Amy Kennedy, B. Lynch, A. Willoughby, V. Helker, A. Brower, J. Clarke
Manned aerial surveys have been used successfully for decades to collect data to infer cetacean distribution, density (number of whales/km2), and abundance. Unmanned aircraft systems (UAS) have potential to augment or replace some manned aerial surveys for cetaceans. We conducted a three-way comparison among visual observations made by marine mammal observers aboard a Turbo Commander aircraft; imagery autonomously collected by a Nikon D810 camera system mounted to a belly port on the Turbo Commander; and imagery collected by a similar camera system on a remotely controlled ScanEagle® UAS operated by the US Navy. Bowhead whale density estimates derived from the marine mammal observer data were higher than those from the Turbo Commander imagery; comparisons to the UAS imagery depended on survey sector and analytical method. Beluga density estimates derived from either dataset collected aboard the Turbo Commander were higher than estimates derived from the UAS imagery. Uncertainties in density estimates derived from the marine mammal observer data were lower than estimates derived from either imagery dataset due to the small sample sizes in the imagery. The visual line-transect aerial survey conducted by marine mammal observers aboard the Turbo Commander was 68.5% of the cost of the photo strip-transect survey aboard the same aircraft and 9.4% of the cost of the UAS survey.
{"title":"Performance of manned and unmanned aerial surveys to collect visual data and imagery for estimating arctic cetacean density and associated uncertainty","authors":"M. Ferguson, R. Angliss, Amy Kennedy, B. Lynch, A. Willoughby, V. Helker, A. Brower, J. Clarke","doi":"10.1139/JUVS-2018-0002","DOIUrl":"https://doi.org/10.1139/JUVS-2018-0002","url":null,"abstract":"Manned aerial surveys have been used successfully for decades to collect data to infer cetacean distribution, density (number of whales/km2), and abundance. Unmanned aircraft systems (UAS) have potential to augment or replace some manned aerial surveys for cetaceans. We conducted a three-way comparison among visual observations made by marine mammal observers aboard a Turbo Commander aircraft; imagery autonomously collected by a Nikon D810 camera system mounted to a belly port on the Turbo Commander; and imagery collected by a similar camera system on a remotely controlled ScanEagle® UAS operated by the US Navy. Bowhead whale density estimates derived from the marine mammal observer data were higher than those from the Turbo Commander imagery; comparisons to the UAS imagery depended on survey sector and analytical method. Beluga density estimates derived from either dataset collected aboard the Turbo Commander were higher than estimates derived from the UAS imagery. Uncertainties in density estimates derived from the marine mammal observer data were lower than estimates derived from either imagery dataset due to the small sample sizes in the imagery. The visual line-transect aerial survey conducted by marine mammal observers aboard the Turbo Commander was 68.5% of the cost of the photo strip-transect survey aboard the same aircraft and 9.4% of the cost of the UAS survey.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/JUVS-2018-0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47682806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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