Autonomous Navigation of UAV in Foliage Environment

Cui, Jinqiang; Lai, Shupeng; Dong, Xiangxu; Chen, Ben M.

doi:10.1007/s10846-015-0292-1

Cited by 39 publications

(16 citation statements)

References 20 publications

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“…In fact, most of the articles reviewed here used multi-rotor systems to monitor understorey vegetation (Table 1). Manoeuvrability is especially important in closed environments, where recent flights in foliage (forested) environments were possible using a quadcopter mounted with a Kalman filter and a 2D laser range finder, in combination with an algorithm used for real-time path planning, to assess obstacle presence and avoid them [54,55]. Cui [54] reported that their real-time object avoidance was possible through very slow flight speeds (0.5 m/s).…”

Section: Sfmmentioning

confidence: 99%

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Hernández-Santín

Rudge

Bartolo³

et al. 2019

Drones

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Understorey vegetation plays an important role in many ecosystems, yet identifying and monitoring understorey vegetation through remote sensing has proved a challenge for researchers and land managers because understorey plants tend to be small, spatially and spectrally similar, and are often blocked by the overstorey. The emergence of Unmanned Aerial Systems (UAS) is revolutionising how vegetation is measured, and may allow us to measure understorey species where traditional remote sensing previously could not. The goal of this paper was to review current literature and assess the current capability of UAS to identify and monitor understorey vegetation. From the literature, we focused on the technical attributes that limit the ability to monitor understorey vegetation—specifically (1) spatial resolution, (2) spectral sensitivity, (3) spatial extent, and (4) temporal frequency at which a sensor acquires data. We found that UAS have provided improved levels of spatial resolution, with authors reporting successful classifications of understorey vegetation at resolutions of between 3 mm and 200 mm. Species discrimination can be achieved by targeting flights to correspond with phenological events to allow the detection of species-specific differences. We provide recommendations as to how UAS attributes can be tailored to help identify and monitor understorey species.

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Section: Sfmmentioning

confidence: 99%

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Hernández-Santín

Rudge

Bartolo³

et al. 2019

Drones

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“…Previous works [7]- [10] focus on autonomous navigation in cluttered [10] or GPS-denied environments [7]. Many works employ UAVs in forests [8]- [14]. Most enable autonomous navigation by detecting and following forest trails using learning-based approaches [9], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Chen

Nardari

Lee

et al. 2020

IEEE Robot. Autom. Lett.

143

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This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.

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“…The FCS requires information on UAV states such as attitude, position, and velocity for control implementation [ 60 ]. The commonly used state observer is the inertial guidance system.…”

Section: Drone Hardware Overviewmentioning

confidence: 99%

A review on drones controlled in real-time

Kangunde

Jamisola

Theophilus

2021

Int. J. Dynam. Control

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This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

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Autonomous Navigation of UAV in Foliage Environment

Cited by 39 publications

References 20 publications

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

A review on drones controlled in real-time

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