While the use of naturally-occurring features is a central focus of machine perception, artificial features (fiducials) play an important role in creating controllable experiments, ground truthing, and in simplifying the development of systems where perception is not the central objective. We describe a new visual fiducial system that uses a 2D bar code style “tag”, allowing full 6 DOF localization of features from a single image. Our system improves upon previous systems, incorporating a fast and robust line detection system, a stronger digital coding system, and greater robustness to occlusion, warping, and lens distortion. While similar in concept to the ARTag system, our method is fully open and the algorithms are documented in detail.

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AprilTag: A robust and flexible visual fiducial system

Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research.

An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion, the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposes an integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-based plume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitive micro-drone. We compare the performance of the proposed system in simulations and real-world experiments against two commonly used tracking algorithms adapted for aerial exploration missions.

Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms

Robots are generally equipped with at least several different modalities of sensors. Vision and range sensors are the most popular, especially in mobile robots. On the other hand, olfaction (or chemical sensing in general) had long been ignored in the robotics community because of the technical difficulties involved in realizing artificial olfaction on robotic platforms. Over the past two decades, however, various attempts are made to use chemical sensors in robotic applications. With the help of chemical sensors, mobile robots can follow chemical trails laid on the ground, track chemical plumes to find their sources, and build distribution maps of chemical substances. This paper is intended to present a brief history and the current trends of the research in this emerging field.

Chemical Sensing in Robotic Applications: A Review

Vision-based tracking is used in nearly all robotic laboratories for monitoring and extracting of agent positions, orientations, and trajectories. However, there is currently no accepted standard software solution available, so many research groups resort to developing and using their own custom software. In this paper, we present version 4 of SwisTrack, an open source project for simultaneous tracking of multiple agents. While its broad range of pre-implemented algorithmic components allows it to be used in a variety of experimental applications, its novelty stands in its highly modular architecture. Advanced users can therefore also implement additional customized modules which extend the functionality of the existing components within the provided interface. This paper introduces SwisTrack and shows experiments with both marked and marker-less agents.

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SwisTrack - a flexible open source tracking software for multi-agent systems

We introduce a novel bio-inspired odor source localization algorithm (surge-cast) for environments with a main wind flow and compare it to two well-known algorithms. With all three algorithms, systematic experiments with real robots are carried out in a wind tunnel under laminar flow conditions. The algorithms are compared in terms of distance overhead when tracking the plume up to the source, but a variety of other experimentally measured results are provided as well. We conclude that the surge-cast algorithm yields significantly better performance than the casting algorithm, and slightly better performance than the surge-spiral algorithm.

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Tracking Odor Plumes in a Laminar Wind Field with Bio-inspired Algorithms

We compare two well-known algorithms for locating odor sources in environments with a main wind flow. Their plume tracking performance is tested through systematic experiments with real robots in a wind tunnel under laminar flow condition. We present the system setup and show the wind and odor profiles. The results are then compared in terms of time and distance to reach the source, as well as speed in upwind direction. We conclude that the spiral-surge algorithm yields significantly better results than the casting algorithm, and discuss possible rationales behind this performance difference.

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A comparison of casting and spiraling algorithms for odor source localization in laminar flow

We compare six different algorithms for localizing odor sources with mobile robots. Three algorithms are bio-inspired and mimic the behavior of insects when exposed to airborne pheromones. Two algorithms are based on probability and information theory, and infer the source location by probabilistically merging concentration measurements at different positions in the environment. The last algorithm is a multi-robot algorithm based on a crosswind line formation. The algorithms are mainly compared with respect to their distance overhead – a metric directly related to the speed of an algorithm – and their success rate. The thesis also reports on the influence of various environmental and algorithmic parameters, and compares the algorithms' requirements regarding sensors, self-localization, maps, and other information. Systematic experiments under laminar flow conditions were carried out with real robots in an 18m long wind tunnel. The robots were thereby equipped with an ethanol sensor and a wind direction sensor, and could – if the algorithm required it – access their current position. Overall, more than 500 experimental runs with teams of up to 5 robots were carried out in this wind tunnel. Similar experiments were also carried out in simulation. Over 5000 runs were carried out in a realistically calibrated multi-robot simulator. Odor was thereby simulated as set of filaments that are transported by advection, an approach that generates the intermittence and stochasticity of real plumes. Additional, more than 10000 runs were carried out using body-less simulators under various plume models. Simulation runs were mostly used to quantify the influence of various parameters on the performance of the algorithms. Finally, the thesis also provides theoretical insights into the bio-inspired algorithms, and a general theoretical model for probabilistic odor source localization. For the latter, a number of potential real-world scenarios are discussed on the example of a simplified train station environment. None of the algorithms is strictly superior to all other algorithms. While the probabilistic algorithms offer more flexibility and a slightly better performance, the bio-inspired algorithms are much less CPU and memory intensive, and could therefore be deployed on extremely small and limited robotic platforms. Using multiple robots (with or without collaboration) for odor source localization was found to improve the performance under certain conditions only. The crosswind formation algorithm with 3 robots yielded excellent results, but the multi-robot experiments with the bio-inspired algorithms were hardly better than their single-robot counterparts. The thesis provides reasons for this, and discusses alternatives.

Bio-inspired and probabilistic algorithms for distributed odor source localization using mobile robots

We derive the theoretical performance of three bio-inspired odor source localization algorithms (casting, surge-spiral and surge-cast) in laminar wind flow. Based on the geometry of the trajectories and the wind direction sensor error, we calculate the distribution of the distance overhead and the mean success rate using Bayes inference. Our approach is related to particle filtering and produces smooth output distributions. The results are compared to existing real-robot and simulation results, and a good match is observed.

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Thomas Lochmatter

Papers

SwisTrack - a flexible open source tracking software for multi-agent systems

Tracking Odor Plumes in a Laminar Wind Field with Bio-inspired Algorithms

A comparison of casting and spiraling algorithms for odor source localization in laminar flow

Bio-inspired and probabilistic algorithms for distributed odor source localization using mobile robots

Theoretical analysis of three bio-inspired plume tracking algorithms