Probabilistic Hough Voting for Attitude Estimation from Aerial Fisheye Images
نویسندگان
چکیده
For navigation of unmanned aerial vehicles (UAVs), attitude estimation is essential. We present a method for attitude estimation (pitch and roll angle) from aerial fisheye images through horizon detection. The method is based on edge detection and a probabilistic Hough voting scheme. In a flight scenario, there is often some prior knowledge of the vehicle altitude and attitude. We exploit this prior to make the attitude estimation more robust by letting the edge pixel votes be weighted based on the probability distributions for the altitude and pitch and roll angles. The method does not require any sky/ground segmentation as most horizon detection methods do. Our method has been evaluated on aerial fisheye images from the internet. The horizon is robustly detected in all tested images. The deviation in the attitude estimate between our automated horizon detection and a manual detection is less than 1◦.
منابع مشابه
Global Pose Estimation from Aerial Images Registration with Elevation Models
Over the last decade, the use of unmanned aerial vehicles (UAVs) has increased drastically. Originally, the use of these aircraft was mainly military, but today many civil applications have emerged. UAVs are frequently the preferred choice for surveillance missions in disaster areas, after earthquakes or hurricanes, and in hazardous environments, e.g. for detection of nuclear radiation. The UAV...
متن کاملInvestigation of Fisheye Lenses for Small UAV Aerial Photography Masters by Research in Electrical Engineering
Aerial photography obtained by UAVs (Unmanned Aerial Vehicles) is an emerging market for civil applications. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs will be able to y at low altitudes, in dangerous environments and over long periods of time. However, the small lightweight constructions of these UAVs le...
متن کاملAll together now: Simultaneous Detection and Continuous Pose Estimation using a Hough Forest with Probabilistic Locally Enhanced Voting
Simultaneous object detection and pose estimation is a challenging task in computer vision. In this paper, we tackle the problem using Hough Forests. Unlike most methods in the literature, we focus on the problem of continuous pose estimation. Moreover, we aim for a probabilistic output. We first introduce a new pose purity criterion for splitting a node during the forest training. Second, we p...
متن کاملStatic Pose Estimation from Depth Images using Random Regression Forests and Hough Voting
Robust and fast algorithms for estimating the pose of a human given an image would have a far reaching impact on many fields in and outside of computer vision. We address the problem using depth data that can be captured inexpensively using consumer depth cameras such as the Kinect sensor. To achieve robustness and speed on a small training dataset, we formulate the pose estimation task within ...
متن کاملAutomatic road extraction from aerial images by probabilistic contour tracking
In this paper a new automatic approach to road extraction from aerial images is proposed. This method improves a recently introduced promising approach to probabilistic contour tracking, originally semi-automatic, by adding a fully automatic initialization strategy and a merging methodology, able to combine the different obtained results. The initialization strategy is based on the Hough transf...
متن کامل