Application and capabilities of lidar from small UAV

Tulldahl, Michael; Larsson, Håkan; Tolt, Gustav; Bissmarck, Fredrik; Grönwall, Christina; Nordlöf, Jonas

doi:10.1117/12.2224258

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…The method is described in more detail in previous publications. 21,22 Although the resulting accuracy is relatively high, the method has the drawback of being computationally expensive and is not implemented for onboard real-time operation on the platform.…”

Section: Operation As a Down-looking Sensormentioning

confidence: 99%

Laser sensing from small UAVs

Tulldahl

Holmberg

Karlsson

et al. 2020

Electro-Optical Remote Sensing XIV

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Currently there is a considerable development of small, lightweight, lidar systems, for applications in autonomous cars. The development gives possibilities to equip small UAVs with this type of sensor. Adding an active sensor component, beside the more common passive UAV sensors, can give additional capabilities. This paper gives experimental examples of lidar data and discusses applications and capabilities for the platform and sensor concept including the combination with data from other sensors. The lidar can be used for accurate 3D measurements and has a potential for detection of partly occluded objects. Additionally, positioning of the UAV can be obtained by a combination of lidar data and data from other low-cost sensors (such as inertial measurement units). The capabilities are attainable both for indoor and outdoor shortrange applications.

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Section: Operation As a Down-looking Sensormentioning

confidence: 99%

Laser sensing from small UAVs

Tulldahl

Holmberg

Karlsson

et al. 2020

Electro-Optical Remote Sensing XIV

Self Cite

View full text Add to dashboard Cite

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“…Over the last decade, advancements in engineering and battery technology have brought consumer UAVs (or drones) into the mainstream opening the door to a host of potential uses of an airborne LiDAR [6] [7]. In particular, combining airborne LiDAR technology with object detection algorithms [8] offers many new possibilities as existing use cases are mainly restricted to static aerial mapping.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Most of the existing research work on small airborne LiDAR is related to terrain mapping, geo-surveying, agricultural robotics and digital elevation mapping. Tulldahl et al [6] review application and capabilities of LiDAR from small UAV but, makes no mention of any object detection or tracking use-case. Commercial solutions exists, for examples the Phoenix LiDAR Systems which aims to provide a complete integrated solution for UAV mounted LiDAR used in mapping and geo-surveying applications [9].…”

Section: Introduction and Related Workmentioning

confidence: 99%

CNN-based Human Detection Using a 3D LiDAR onboard a UAV

Hayton

Barros

Premebida

et al. 2020

2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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This paper addresses the problem of detecting humans in a point cloud taken with a 3D-LiDAR onboard a UAV. The potential use cases of this technology are numerous, examples include security and surveillance, disaster relief and search and rescue operations. In this paper, a CNN-based approach is proposed which is able to analyse point clouds returned by a 3D LiDAR sensor in such a way that it can detect humans. The algorithm described here consists of 3 main components: data pre-processing, post-processing, and human classification. In this paper objects were assigned to one of two classes: human and non-human. The classification was performed by projecting the 3D point cloud onto a series of 2D planes using occupancy grid mapping. This creates a set of silhouettes of the object corresponding to the top, front and side views. Classification is achieved by supervised CNNs using single-view and multi-view (3 channels) images patches. Raw DataRaw data from airborne LiDAR Data Synch. LOAM-based LiDAR Point-Cloud synchronization Ground detection RANSAC to determine the ground plane Data Alignment Align Point-cloud w/ reference coordinate axes Define ROI Use reference point to define rectangular ROI Clustering Separate Pointcloud into clusters Occ. Grid Project the Points onto 2D Grid map Process Image Convert grid to 2D image at cluster location Human classification CNN-based classifier Point cloud pre-processing Post-processing CNN-based classification

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Application and capabilities of lidar from small UAV

Cited by 2 publications

References 4 publications

Laser sensing from small UAVs

Laser sensing from small UAVs

CNN-based Human Detection Using a 3D LiDAR onboard a UAV

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