Evaluation of low-cost LiDAR sensor for application in indoor UAV navigation

Jeong, Nulee; Hwang, Hyun; Matson, Eric T.

doi:10.1109/sas.2018.8336719

Cited by 40 publications

(21 citation statements)

References 5 publications

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“…For example, vision-based models using different visual techniques such as visual odometry (VO), simultaneous localization and mapping (SLAM), and optical flow [3]- [5]. There are also a few research papers where they use deep neural networks in combination with visual techniques [13] or use of LiDAR [14] for autonomous flying.…”

Section: Related Workmentioning

confidence: 99%

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

Famili¹,

Stavrou²,

Wang³

et al. 2022

Preprint

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For many applications, drones are required to operate entirely or partially autonomously. To fly completely or partially on their own, drones need access to location services to get navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the popular methods for localization, specially for indoor environments. In general, localization error in this class is due to two factors: the ranging error and the error induced by the relative geometry between the beacons and the target object to localize. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in threedimensional indoor environments. OPTILOD leverages advances in Evolutionary Algorithms to compute the minimum number of beacons and their optimal placement to minimize the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-Hard. Despite that, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and time efficiently.

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Section: Related Workmentioning

confidence: 99%

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

Famili¹,

Stavrou²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The major drawback of using merely visual techniques is poor image quality due to the obstacles in a drone's flying environment, which degrade the accuracy. There are a few research papers where they tried to resolve this issue by leveraging some auxiliary techniques, e.g., some used deep neural networks in combination with visual techniques (e.g., [13]) or some gained benefits from the use of LiDAR (e.g., [9]) for autonomous flying.…”

Section: Related Workmentioning

confidence: 99%

iDROP: Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement

Famili¹,

Stavrou²,

Wang³

et al. 2022

Preprint

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Drones in many applications need the ability to fly fully or partially autonomously to accomplish their mission. To allow these fully/partially autonomous flights, first, the drone needs to be able to locate itself constantly. Then the navigation command signal would be generated and passed on to the controller unit of the drone. In this paper, we propose a localization scheme for drones called iDROP (Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement) that is specifically devised for GPSdenied environments (e.g., indoor spaces). Instead of GPS signals, iDROP relies on speaker-generated ultrasonic acoustic signals to enable a drone to estimate its location. In general, localization error is due to two factors: the ranging error and the error induced by relative geometry between the transmitters and the receiver. iDROP mitigates these two types of errors and provides a high-precision three-dimensional localization scheme for drones. iDROP employs a waveform that is robust against multi-path fading. Moreover, by placing beacons in optimal locations, it reduces the localization error induced by the relative geometry between the transmitters and the receiver.

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“…The laser scanning conducted via a UAV platform can also be categorized into the ALS. Thanks to the smaller size and lighter weight of its platform, UAV-based ALS can offer a high operational flexibility [2], which can even be used in an indoor scenario [20]. One major disadvantage of ALS is the instability of the working platform, meaning that the point clouds acquired by ALS normally have a relatively low geo-positioning accuracy; in some situations, motion compensation is also required for the data processing.…”

Section: Differences Between Lidar Point Clouds Acquired With Various...mentioning

confidence: 99%

LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas

Huang

et al. 2020

IJGI

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The semantic labeling of the urban area is an essential but challenging task for a wide variety of applications such as mapping, navigation, and monitoring. The rapid advance in Light Detection and Ranging (LiDAR) systems provides this task with a possible solution using 3D pointclouds, which are accessible, affordable, accurate, and applicable. Among all types of platforms, the airborne platform with LiDAR can serve as an efficient and effective tool for large-scale 3D mapping in the urban area. Against this background, a large number of algorithms and methods have been developed to fully explore the potential of 3D point clouds. However, the creation ofpublicly accessible large-scale annotated datasets, which are critical for assessing the performance of the developed algorithms and methods, is still at an early age. In this work, we present a large-scale aerial LiDAR point cloud dataset acquired in a highly-dense and complex urban area for the evaluation of semantic labeling methods. This dataset covers an urban area with highly-dense buildings of approximately 1 km2 and includes more than 3 million points with five classes of objects labeled. Moreover, experiments are carried out with the results from several baseline methods, demonstrating the feasibility and capability of the dataset serving as a benchmark for assessing semantic labeling methods.

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Evaluation of low-cost LiDAR sensor for application in indoor UAV navigation

Cited by 40 publications

References 5 publications

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

iDROP: Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement

LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas

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