DeepOSM-3D: recognition in aerial LiDAR RGBD imagery

Rajagopal, Abhejit; Stier, Noah; Nelson, William E.; Chandrasekaran, Shivkumar; Brown, Alexandra

doi:10.1117/12.2565585

Cited by 3 publications

(2 citation statements)

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“…All these, individually or in combination, give an accurate 2D aerial map delineating the positions of obstacles. In addition, airborne 3D LiDAR [20] fitted on the drone can proffer depth readings that can inversely signify the height of the various obstacles. Fusing such multi-sensor information, by methods like bimodal learning [21] from RGB-D information or unified co-attention networks [22] can augment the spatial maps with depth information.…”

Section: Perception and Real-time Formation Of Mapsmentioning

confidence: 99%

AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

Jana¹,

Jana²

2021

Preprint

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With technological advancement, drone has emerged as unmanned aerial vehicle that can be controlled by humans to fly or reach a destination. This may be autonomous as well, where the drone itself is intelligent enough to find a shortest obstacle-free path to reach the destination from a designated source. Be it a planned smart city or even a wreckage site affected by natural calamity, we may imagine the buildings, any surface-erected structure or other blockage as obstacles for the drone to fly in a straight line-of-sight path. To address such path-planning of drones, the bird's eye-view of the whole landscape is first transformed to a graph of grid-cells, where some are occupied to indicate the obstacles and some are free to indicate the free path. We propose a method to find out the shortest obstacle-free path in the coordinate system guided by GPS. The autonomous drone (AutoDrone) will thus be able to move from one place to another along the shortest path, without colliding into hindrances, while traveling in a two-dimensional space. Heuristics to extend this to long journeys and 3D space are also elaborated. Our approach can be especially beneficial in rescue operations and fast delivery or pick-up in an energy-efficient way, where our algorithm will help in finding out the shortest path and angle along which it should fly. Experiments are done on different scenarios of map layouts and obstacle positions to understand the shortest feasible route, computed by the autonomous drone.

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Section: Perception and Real-time Formation Of Mapsmentioning

confidence: 99%

AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

Jana¹,

Jana²

2021

Preprint

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“…True 3D imaging of targets is highly desirable in many intelligence, reconnaissance, and surveillance missions because it reveals geometric information about targets and target-scenes that aid in automatic target recognition, navigation, threat-level assessment, and strategic planning. 1,2 Compared to 3D reconstruction from airborne or satellite electro-optic (EO) sensors, 3D synthetic aperture radar (SAR) imaging offers the promise of enhanced target-scene characterization in all weather (day and night), from long range, and from single or limited viewing perspectives with short dwell times, enhancing the feasibility of obtaining up-to-date target-scene models in-flight.…”

Section: Introductionmentioning

confidence: 99%

Enhanced compressed sensing 3D SAR imaging via cross-modality EO-SAR joint-sparsity priors

Rajagopal

Hilton

Boutte

et al. 2023

Algorithms for Synthetic Aperture Radar Imagery XXX

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We introduce a compressed sensing technique for leveraging prior electro-optic (EO) imagery to improve 3D synthetic aperture radar (SAR) imaging performance. Specifically, we build on existing iterative reconstruction algorithms by guiding the reconstruction process with a joint-sparsity regularization term that captures the complementary structural information shared between EO and SAR via a sparsifying transform in the 3D image domain. We demonstrate this approach using the wavelet transform, the non-uniform Fast-Fourier transform (NUFFT), and optimizers built on autograd utilizing the 2004 AFRL Gotcha SAR dataset, with complementary EO imagery collected from the 2013 Minor Area Motion Imagery (MAMI) collection and more recent (2016) satellite collections over the same area. Results indicate significant improvements in 2D and 3D imaging performance via incorporation of the cross-modality EO prior, which we attribute to the convex problem formulation.

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AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

Jana¹,

Jana²

2022

Algorithms for Intelligent Systems

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DeepOSM-3D: recognition in aerial LiDAR RGBD imagery

Cited by 3 publications

References 0 publications

AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

Enhanced compressed sensing 3D SAR imaging via cross-modality EO-SAR joint-sparsity priors

AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

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