Object Depth Measurement and Filtering from Monocular Images for Unmanned Aerial Vehicles
- 1 March 2022
- journal article
- research article
- Published by American Institute of Aeronautics and Astronautics (AIAA) in Journal of Aerospace Information Systems
- Vol. 19 (3), 214-223
- https://doi.org/10.2514/1.i011022
Abstract
The flight safety of low-altitude small fixed-wing unmanned aerial vehicles (UAVs) is often threatened by obstacles such as buildings. This requires UAVs to have the ability to autonomously measure the depth of objects ahead. However, existing depth measurement methods based on multiview geometry and handcrafted features still have problems in accuracy and scene suitability. This paper proposes an object depth measurement and filtering method for UAVs by using monocular images. Firstly, the length of the line segment between feature points instead of the pixel position of feature point is used to solve object depth, which reduces the adverse effect of feature matching error. Meanwhile, in order to adapt to UAV platforms, height and attitude changes are both considered in the modeling process. Moreover, the sequence of object depth values corresponding to the image sequence is filtered by an extended Kalman filter to reduce oscillations. The effectiveness of the whole scheme is verified by visual simulation. Results show that the proposed method achieves better accuracy than other depth measurement methods based on multiview geometry.Keywords
Funding Information
- Postgraduate Research Practice Innovation Program of Jiangsu Province (KYCX19_0194)
- Open Project Funds for the Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology (NJ2020021-01)
- Fundamental Research Funds for the Central Universities (NJ2020021)
- National Natural Science Foundation of China (61673211, U2033201)
- Nanjing University of Aeronautics and Astronautics PhD short-term visiting scholar project (ZDGB2021035)
This publication has 17 references indexed in Scilit:
- Candidate regions extraction of intruder airplane under complex background for vision‐based sense and avoid systemIET Science, Measurement & Technology, 2017
- An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT FeatureApplied Sciences, 2017
- A survey of structure from motion.Acta Numerica, 2017
- The inherent ambiguity in scene depth learning from single\\ imagesSCIENTIA SINICA Informationis, 2016
- Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range PhotogrammetryPhotogrammetric Engineering & Remote Sensing, 2015
- Vision meets robotics: The KITTI datasetThe International Journal of Robotics Research, 2013
- Simultaneous localization and mapping: part IIEEE Robotics & Automation Magazine, 2006
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- TriangulationComputer Vision and Image Understanding, 1997
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960