Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162468
Title: A self-supervised monocular depth estimation approach based on UAV aerial images
Authors: Zhang, Yuhang
Yu, Qing
Low Kin Huat 
Lv, Chen 
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Zhang, Y., Yu, Q., Low Kin Huat & Lv, C. (2022). A self-supervised monocular depth estimation approach based on UAV aerial images. 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC). https://dx.doi.org/10.1109/DASC55683.2022.9925733
Abstract: The Unmanned Aerial Vehicles (UAVs) have gained increasing attention recently, and depth estimation is one of the essential tasks for the safe operation of UAVs, especially for drones at low altitudes. Considering the limitations of UAVs’ size and payload, innovative methods combined with deep learning techniques have taken the place of traditional sensors to become the mainstream for predicting per-pixel depth information. Since supervised depth estimation methods require a massive amount of depth ground truth as the supervisory signal. This article proposes an unsupervised framework to tackle the issue of predicting the depth map given a sequence of monocular images. Our model can solve the problem of scale ambiguity by training the depth subnetwork jointly with the pose subnetwork. Moreover, we introduce a modified loss function that utilizes a weighted photometric loss combined with the edge-aware smoothness loss to optimize the training. The evaluation results are compared with the model without weighted loss and other unsupervised monocular depth estimation models (Monodepth and Monodepth2). Our model shows better performance than the others, indicating potential assistance in enhancing the capability of UAVs to estimate distance with the surrounding environment.
URI: https://hdl.handle.net/10356/162468
ISSN: 2155-7209
DOI: 10.1109/DASC55683.2022.9925733
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/DASC55683.2022.9925733.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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