23,760 research outputs found
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
Towards Declarative Safety Rules for Perception Specification Architectures
Agriculture has a high number of fatalities compared to other blue collar
fields, additionally population decreasing in rural areas is resulting in
decreased work force. These issues have resulted in increased focus on
improving efficiency of and introducing autonomy in agriculture. Field robots
are an increasingly promising branch of robotics targeted at full automation in
agriculture. The safety aspect however is rely addressed in connection with
safety standards, which limits the real-world applicability. In this paper we
present an analysis of a vision pipeline in connection with functional-safety
standards, in order to propose solutions for how to ascertain that the system
operates as required. Based on the analysis we demonstrate a simple mechanism
for verifying that a vision pipeline is functioning correctly, thus improving
the safety in the overall system.Comment: Presented at DSLRob 2015 (arXiv:1601.00877
Towards the development of a smart flying sensor: illustration in the field of precision agriculture
Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology
Unmanned Aerial Vehicle (UAV) for monitoring soil erosion in Morocco
This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAVâs log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents
Trying to break new ground in aerial archaeology
Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earthâs atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection
An Effective Multi-Cue Positioning System for Agricultural Robotics
The self-localization capability is a crucial component for Unmanned Ground
Vehicles (UGV) in farming applications. Approaches based solely on visual cues
or on low-cost GPS are easily prone to fail in such scenarios. In this paper,
we present a robust and accurate 3D global pose estimation framework, designed
to take full advantage of heterogeneous sensory data. By modeling the pose
estimation problem as a pose graph optimization, our approach simultaneously
mitigates the cumulative drift introduced by motion estimation systems (wheel
odometry, visual odometry, ...), and the noise introduced by raw GPS readings.
Along with a suitable motion model, our system also integrates two additional
types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random
Field assumption. We demonstrate how using these additional cues substantially
reduces the error along the altitude axis and, moreover, how this benefit
spreads to the other components of the state. We report exhaustive experiments
combining several sensor setups, showing accuracy improvements ranging from 37%
to 76% with respect to the exclusive use of a GPS sensor. We show that our
approach provides accurate results even if the GPS unexpectedly changes
positioning mode. The code of our system along with the acquired datasets are
released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters,
201
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