Foresight: Remote Sensing for Autonomous Vehicles Using a Small Unmanned Aerial Vehicle

Wallar, Alex; Araki, Brandon; Chang, Raphael; Alonso–Mora, Javier; Rus, Daniela

doi:10.1007/978-3-319-67361-5_38

Cited by 11 publications

(13 citation statements)

References 14 publications

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“…However, recent developments in the technology of UAV-RS have opened up some new possibilities of applications, e.g. pedestrian behavior understanding [35], intelligent driving and path planning [36], which have not been reviewed.…”

Section: Related To Previous Surveysmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

164

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The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, highspatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remotesensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.

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Section: Related To Previous Surveysmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

164

View full text Add to dashboard Cite

show abstract

“…However, the approach of [1] still depends on infrastructure because the quadrotor control relies on Mocap or GPS data instead of the localization feedback. In [2], a quadrotor searches for safe paths for a ground vehicle and localizes itself with respect to the vehicle by unscented Kalman filter and optimization techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Trilateration method takes a set of anchor-sensor distance measurements at a particular time instant and produces a closed-form solution for possible sensor locations based on the distance geometry [22]- [24]. Similarly, optimization methods minimize the additive noise on a set of anchor-sensor distances, subject to equalities obtained from the geometric structure [2], [25]. However, neither approach is preferred for mobile robot localization standalone because they suffer from measurement noises.…”

Section: Related Workmentioning

confidence: 99%

Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors

Güler

Abdelkader

Shamma

2021

IEEE Trans. Contr. Syst. Technol.

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Robots in swarms take advantage of localization infrastructure such as a motion capture system or GPS sensors to obtain their global position, which can then be communicated to other robots for swarm coordination. However, the availability of localization infrastructure need not be guaranteed, e.g., in GPS denied environments. Likewise, the communication overhead associated with broadcasting locations may be undesirable. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard relative localization framework for multi-robot systems. The setup consists of an anchor robot with three onboard ultrawideband (UWB) sensors and a tag robot with a single onboard UWB sensor. The anchor robot utilizes the three UWB sensors to estimate the tag robot's location by using its onboard sensing and computational capabilities solely, without explicit inter-robot communication. Because the anchor UWB sensors lack the physical separation that is typical in fixed UWB localization systems, we introduce filtering methods to improve estimation of the tag's location. In particular, we utilize a mixture Monte-Carlo Localization (MCL) approach to capture maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor field experiments on a two-drone setup. The proposed mixture MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates superior performance over the standard particle filter and the extended Kalman filter.

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“…This section covers related works and methods previously used to see past or through occlusions (non-lineof-sight (NLoS) problem) and vision based ADAS. Proposed NLoS solutions range from WiFi signals [1], to exploiting specular surfaces [30], [7] and drones [27]. Our presented work does not rely on any infrastructure, hardware or material assumptions.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

Naser

Gilitschenski

Amini

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Current perception systems mostly require direct line of sight to anticipate and ultimately prevent potential collisions at intersections with other road users. We present a fully integrated autonomous system capable of detecting shadows or weak illumination changes on the ground caused by a dynamic obstacle in NLoS scenarios. This additional virtual sensor "ShadowCam" extends the signal range utilized so far by computer-vision ADASs. We show that (1) our algorithm maintains the mean classification accuracy of around 70% even when it doesn't rely on infrastructure -such as AprilTagsas an image registration method. We validate (2) in real-world experiments that our autonomous car driving in night time conditions detects a hidden approaching car earlier with our virtual sensor than with the front facing 2-D LiDAR.

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Foresight: Remote Sensing for Autonomous Vehicles Using a Small Unmanned Aerial Vehicle

Cited by 11 publications

References 14 publications

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors

Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

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