# Towards Deep Learning Assisted Autonomous UAVs for Manipulation Tasks in GPS-Denied Environments

arXiv: https://arxiv.org/abs/2101.06414 · HTML: https://mlsanity.com/paper/2101.06414/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 16 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection](https://mlsanity.com/paper/1606.05413/) (2016)

## Built on by (most cited first, 3 of 3)

- [High-Speed Stereo Visual SLAM for Low-Powered Computing Devices](https://mlsanity.com/paper/2410.04090/) (2024)
- [Pick-or-Mix: Dynamic Channel Sampling for ConvNets](https://mlsanity.com/paper/2406.10935/) (2024)
- [High-Speed Detector For Low-Powered Devices In Aerial Grasping](https://mlsanity.com/paper/2402.14591/) (2024)

## Abstract (arXiv)

In this work, we present a pragmatic approach to enable unmanned aerial vehicle (UAVs) to autonomously perform highly complicated tasks of object pick and place. This paper is largely inspired by challenge-2 of MBZIRC 2020 and is primarily focused on the task of assembling large 3D structures in outdoors and GPS-denied environments. Primary contributions of this system are: (i) a novel computationally efficient deep learning based unified multi-task visual perception system for target localization, part segmentation, and tracking, (ii) a novel deep learning based grasp state estimation, (iii) a retracting electromagnetic gripper design, (iv) a remote computing approach which exploits state-of-the-art MIMO based high speed (5000Mb/s) wireless links to allow the UAVs to execute compute intensive tasks on remote high end compute servers, and (v) system integration in which several system components are weaved together in order to develop an optimized software stack. We use DJI Matrice-600 Pro, a hex-rotor UAV and interface it with the custom designed gripper. Our framework is deployed on the specified UAV in order to report the performance analysis of the individual modules. Apart from the manipulation system, we also highlight several hidden challenges associated with the UAVs in this context.