998 research outputs found
A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots
Legged robots are becoming popular not only in research, but also in
industry, where they can demonstrate their superiority over wheeled machines in
a variety of applications. Either when acting as mobile manipulators or just as
all-terrain ground vehicles, these machines need to precisely track the desired
base and end-effector trajectories, perform Simultaneous Localization and
Mapping (SLAM), and move in challenging environments, all while keeping
balance. A crucial aspect for these tasks is that all onboard sensors must be
properly calibrated and synchronized to provide consistent signals for all the
software modules they feed. In this paper, we focus on the problem of
calibrating the relative pose between a set of cameras and the base link of a
quadruped robot. This pose is fundamental to successfully perform sensor
fusion, state estimation, mapping, and any other task requiring visual
feedback. To solve this problem, we propose an approach based on factor graphs
that jointly optimizes the mutual position of the cameras and the robot base
using kinematics and fiducial markers. We also quantitatively compare its
performance with other state-of-the-art methods on the hydraulic quadruped
robot HyQ. The proposed approach is simple, modular, and independent from
external devices other than the fiducial marker.Comment: To appear on "The Third IEEE International Conference on Robotic
Computing (IEEE IRC 2019)
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
We propose a new method to estimate the 6-dof trajectory of a flying object
such as a quadrotor UAV within a 3D airspace monitored using multiple fixed
ground cameras. It is based on a new structure from motion formulation for the
3D reconstruction of a single moving point with known motion dynamics. Our main
contribution is a new bundle adjustment procedure which in addition to
optimizing the camera poses, regularizes the point trajectory using a prior
based on motion dynamics (or specifically flight dynamics). Furthermore, we can
infer the underlying control input sent to the UAV's autopilot that determined
its flight trajectory.
Our method requires neither perfect single-view tracking nor appearance
matching across views. For robustness, we allow the tracker to generate
multiple detections per frame in each video. The true detections and the data
association across videos is estimated using robust multi-view triangulation
and subsequently refined during our bundle adjustment procedure. Quantitative
evaluation on simulated data and experiments on real videos from indoor and
outdoor scenes demonstrates the effectiveness of our method
Use of the Sentinel‐2 and Landsat‐8 Satellites for Water Quality Monitoring: An Early Warning Tool in the Mar Menor Coastal Lagoon
During recent years, several eutrophication processes and subsequent environmental crises have occurred in Mar Menor, the largest hypersaline coastal lagoon in the Western Mediterranean Sea. In this study, the Landsat‐8 and Sentinel‐2 satellites are jointly used to examine the evolution of the main water quality descriptors during the latest ecological crisis in 2021, resulting in an important loss of benthic vegetation and unusual mortality events affecting different aquatic species. Several field campaigns were carried out in March, July, August, and November 2021 to measure water quality variables over 10 control points. The validation of satellite biogeochemical variables against on‐site measurements indicates precise results of the water quality algorithms with median errors of 0.41 mg/m3 and 2.04 FNU for chlorophyll‐a and turbidity, respectively. The satellite preprocessing scheme shows consistent performance for both satellites; therefore, using them in tandem can improve mapping strategies. The findings demonstrate the suitability of the methodology to capture the spatiotemporal distribution of turbidity and chlorophyll‐a concentration at 10– 30 m spatial resolution on a systematic basis and in a cost‐effective way. The multitemporal products allow the identification of the main critical areas close to the mouth of the Albujon watercourse and the beginning of the eutrophication process with chlorophyll‐a concentration above 3 mg/m3. These innovative tools can support decision makers in improving current monitoring strategies as early warning systems for timely assistance during these ecological disasters, thus preventing detrimental conditions in the lagoon.0,64
External multi-modal imaging sensor calibration for sensor fusion: A review
Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0
Detection of leaf structures in close-range hyperspectral images using morphological fusion
Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods
Estimating Precipitation from WSR-88D Observations and Rain Gauge Data: Potential for Drought Monitoring
Since its deployment, the precipitation estimates from the network of National Weather Service (NWS) Weather Surveillance Radars-1988 Doppler (WSR-88D) have become widely used. These precipitation estimates are used for the flash flood warning program at NWS Weather Forecast Offices (WFOs) and the hydrologic program at NWS River Forecast Centers (RFCs), and they also show potential as an input data set for drought monitoring. However, radar-based precipitation estimates can contain considerable error because of radar limitations such as range degradation and radar beam blockage or false precipitation estimates from anomalous propagation (AP) of the radar beam itself. Because of these errors, for operational applications, the RFCs adjust the WSR-88D precipitation estimates using a multisensor approach. The primary goal of this approach is to reduce both areal-mean and local bias errors in radar-derived precipitation by using rain gauge data so that the final estimate of rainfall is better than an estimate from a single sensor.
This chapter briefly discusses the past efforts for estimating mean areal precipitation (MAP). Although there are currently several radar and rain gauge estimation techniques, such as Process 3, Mountain Mapper, and Daily Quality Control (QC), this chapter will emphasize the Multisensor Precipitation Estimator (MPE) Precipitation Processing System (PPS). The challenges faced by the Hydrometeorological Analysis and Support (HAS) forecasters at RFCs to quality control all sources of precipitation data in the MPE program, including the WSR-88D estimates, will be discussed. The HAS forecaster must determine in real time if a particular radar is correctly estimating, overestimating, or underestimating precipitation and make adjustments within the MPE program so the proper amount of precipitation is determined. In this chapter, we discuss procedures used by the HAS forecasters to improve initial best estimates of precipitation using 24 h rain gauge data, achieving correlation coefficients greater than 0.85. Finally, since several organizations are now using the output of MPE for deriving short- and long-term Standardized Precipitation Indices (SPIs), this chapter will discuss how spatially distributed estimates of precipitation can be used for drought monitoring
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