2,627 research outputs found
Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments
This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras.
While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation.
Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications
Perception-aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments
Tag-based visual-inertial localization is a lightweight method for enabling
autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs)
in indoor construction environments. However, finding the optimal tag
configuration (i.e., number, size, and location) on dynamic construction sites
remains challenging. This paper proposes a perception-aware genetic
algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag
configuration using 4D-BIM, considering the project progress, safety
requirements, and UAV's localizability. The proposed method provides a 4D plan
for tag placement by maximizing the localizability in user-specified regions of
interest (ROIs) while limiting the installation costs. Localizability is
quantified using the Fisher information matrix (FIM) and encapsulated in
navigable grids. The experimental results show the effectiveness of our method
in finding an optimal 4D tag placement plan for the robust localization of UAVs
on under-construction indoor sites.Comment: [Final draft] This material may be downloaded for personal use only.
Any other use requires prior permission of the American Society of Civil
Engineers and the Journal of Computing in Civil Engineerin
Airborne chemical sensing with mobile robots
Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations
Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment
Many localization algorithms use a spatiotemporal window of sensory
information in order to recognize spatial locations, and the length of this
window is often a sensitive parameter that must be tuned to the specifics of
the application. This letter presents a general method for environment-driven
variation of the length of the spatiotemporal window based on searching for the
most significant localization hypothesis, to use as much context as is
appropriate but not more. We evaluate this approach on benchmark datasets using
visual and Wi-Fi sensor modalities and a variety of sensory comparison
front-ends under in-order and out-of-order traversals of the environment. Our
results show that the system greatly reduces the maximum distance traveled
without localization compared to a fixed-length approach while achieving
competitive localization accuracy, and our proposed method achieves this
performance without deployment-time tuning.Comment: Pre-print of article appearing in 2017 IEEE Robotics and Automation
Letters. v2: incorporated reviewer feedbac
Experimental analysis of gas-sensitive Braitenberg vehicles
This article addresses the problem of localizing a static gas source in an indoor environment by a mobile robot. In contrast to previous works, the environment is not artificially ventilated to produce a strong unidirectional airflow. Here, the dominant transport mechanisms of gas molecules are turbulence and convection flow rather than diffusion, which results in a patchy, chaotically fluctuating gas distribution. Two Braitenberg-type strategies (positive and negative tropotaxis) based on the instantaneously measured spatial concentration gradient were investigated. Both strategies were shown to be of potential use for gas source localization. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localization strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions
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