76,954 research outputs found
A Low Cost UWB Based Solution for Direct Georeferencing UAV Photogrammetry
Thanks to their flexibility and availability at reduced costs, Unmanned Aerial Vehicles (UAVs) have been recently used on a wide range of applications and conditions. Among these, they can play an important role in monitoring critical events (e.g., disaster monitoring) when the presence of humans close to the scene shall be avoided for safety reasons, in precision farming and surveying. Despite the very large number of possible applications, their usage is mainly limited by the availability of the Global Navigation Satellite System (GNSS) in the considered environment: indeed, GNSS is of fundamental importance in order to reduce positioning error derived by the drift of (low-cost) Micro-Electro-Mechanical Systems (MEMS) internal sensors. In order to make the usage of UAVs possible even in critical environments (when GNSS is not available or not reliable, e.g., close to mountains or in city centers, close to high buildings), this paper considers the use of a low cost Ultra Wide-Band (UWB) system as the positioning method. Furthermore, assuming the use of a calibrated camera, UWB positioning is exploited to achieve metric reconstruction on a local coordinate system. Once the georeferenced position of at least three points (e.g., positions of three UWB devices) is known, then georeferencing can be obtained, as well. The proposed approach is validated on a specific case study, the reconstruction of the façade of a university building. Average error on 90 check points distributed over the building façade, obtained by georeferencing by means of the georeferenced positions of four UWB devices at fixed positions, is 0.29 m. For comparison, the average error obtained by using four ground control points is 0.18 m
COMPARISON OF LOW COST PHOTOGRAMMETRIC SURVEY WITH TLS AND LEICA PEGASUS BACKPACK 3D MODELS
This paper considers Leica backpack and photogrammetric surveys of a mediaeval bastion in Padua, Italy. Furhtermore, terrestrial
laser scanning (TLS) survey is considered in order to provide a state of the art reconstruction of the bastion. Despite control points
are typically used to avoid deformations in photogrammetric surveys and ensure correct scaling of the reconstruction, in this paper
a different approach is considered: this work is part of a project aiming at the development of a system exploiting ultra-wide band
(UWB) devices to provide correct scaling of the reconstruction. In particular, low cost Pozyx UWB devices are used to estimate
camera positions during image acquisitions. Then, in order to obtain a metric reconstruction, scale factor in the photogrammetric
survey is estimated by comparing camera positions obtained from UWB measurements with those obtained from photogrammetric
reconstruction. Compared with the TLS survey, the considered photogrammetric model of the bastion results in a RMSE of 21.9cm, average error 13.4cm, and standard deviation 13.5cm. Excluding the final part of the bastion left wing, where the presence of several poles make reconstruction more difficult, (RMSE) fitting error is 17.3cm, average error 11.5cm, and standard deviation 9.5cm. Instead, comparison of Leica backpack and TLS surveys leads to an average error of 4.7cm and standard deviation 0.6cm (4.2 cm and 0.3 cm, respectively, by excluding the final part of the left wing)
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
In the last decade, supervised deep learning approaches have been extensively
employed in visual odometry (VO) applications, which is not feasible in
environments where labelled data is not abundant. On the other hand,
unsupervised deep learning approaches for localization and mapping in unknown
environments from unlabelled data have received comparatively less attention in
VO research. In this study, we propose a generative unsupervised learning
framework that predicts 6-DoF pose camera motion and monocular depth map of the
scene from unlabelled RGB image sequences, using deep convolutional Generative
Adversarial Networks (GANs). We create a supervisory signal by warping view
sequences and assigning the re-projection minimization to the objective loss
function that is adopted in multi-view pose estimation and single-view depth
generation network. Detailed quantitative and qualitative evaluations of the
proposed framework on the KITTI and Cityscapes datasets show that the proposed
method outperforms both existing traditional and unsupervised deep VO methods
providing better results for both pose estimation and depth recovery.Comment: ICRA 2019 - accepte
High-Performance and Tunable Stereo Reconstruction
Traditional stereo algorithms have focused their efforts on reconstruction
quality and have largely avoided prioritizing for run time performance. Robots,
on the other hand, require quick maneuverability and effective computation to
observe its immediate environment and perform tasks within it. In this work, we
propose a high-performance and tunable stereo disparity estimation method, with
a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can
potentially enable robots to quickly reconstruct their immediate surroundings
and maneuver at high-speeds. Our key contribution is a disparity estimation
algorithm that iteratively approximates the scene depth via a piece-wise planar
mesh from stereo imagery, with a fast depth validation step for semi-dense
reconstruction. The mesh is initially seeded with sparsely matched keypoints,
and is recursively tessellated and refined as needed (via a resampling stage),
to provide the desired stereo disparity accuracy. The inherent simplicity and
speed of our approach, with the ability to tune it to a desired reconstruction
quality and runtime performance makes it a compelling solution for applications
in high-speed vehicles.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 2016; 8 pages, 5 figure
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