Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation

Segura, Marcelo; Cheein, Fernando Auat; Toibero, Juan Marcos; Mut, Vicente; Carelli, Ricardo

doi:10.3390/s110202035

Cited by 29 publications

(12 citation statements)

References 18 publications

(23 reference statements)

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“…Many non-visual sensor based (non-vision based) techniques such as Global Positioning System (GPS) [20], Radio Frequency Identification (RFID) [21,22], Wireless Local Area Network (WLAN) [21,23], UltraWide Band (UWB) [24], Bluetooth [25], and Inertial Measurement Unit (IMU) [26] used to be the primary focus of many studies for localization and navigation indoors. Though GPS and UWB have good accuracy, they require Line Of Sight (LOS) and tend to be costly.…”

Section: Methodsmentioning

confidence: 99%

Ambient Data Collection in Indoor Building Environments Using Mobile Robots

Mantha

Menassa²,

Kamat

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Building designers have increasingly looked at simulation to improve the performance of building systems with the primary objective of simultaneously maximizing comfort, and minimizing energy use. The quality of the simulations depends on the quality of the data being input into the models. The input data for simulation comprises the current real state of a building which encompasses several components that include the state of occupants, comfort parameters, and the building systems. Typically, such data is aggregated using preinstalled Building Automation Systems (BAS) with the help of stationary wired or wireless sensor networks. Such a process is cost-prohibitive, time consuming, and often impractical in existing buildings without BAS. This paper proposes mobile platform based robotic data collection for gathering energy and comfort related data in real-time which can be utilized for further simulation analysis and decision-making. The fiducial marker based navigation and drift correction algorithms developed to facilitate the robotic platform navigation in a building are discussed in detail. This method successfully achieves the navigation task by providing directional navigation information along with drift correction at critical discrete locations instead of the traditional continuous updating process, which is computationally intensive. An experimental study validating the statistical equivalence of the two data sets gathered by the traditional preinstalled fixed sensor networks and the multi-sensor fused robot was performed. The results demonstrate the feasibility of the proposed methodology in efficiently collecting large datasets in buildings using only a single set of sensors in contrast to the scads of similar sensors required by traditional data collection methods.

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Section: Methodsmentioning

confidence: 99%

Ambient Data Collection in Indoor Building Environments Using Mobile Robots

Mantha

Menassa²,

Kamat

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Segura et al implemented a functional SLAM and localization system using UWB radar sensor [4]. The receiver on a mobile robot measured waves from multiple fixed transmitting antennas and estimated the transmit antenna positions and the robot position using an extended Kalman filter (EKF).…”

Section: Related Workmentioning

confidence: 99%

“…Several localization methods using UWB radar have been proposed [4,5,6,7,8]. However, almost all of these methods rely on augmenting the environment with artificial beacons.…”

mentioning

confidence: 99%

Localization and Place Recognition Using an Ultra-Wide Band (UWB) Radar

Takeuchi

Elfes

Roberts

2015

Springer Tracts in Advanced Robotics

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Abstract. This paper presents an approach to mobile robot localization, place recognition and loop closure using a monostatic ultra-wide band (UWB) radar system. The UWB radar is a time-of-flight based range measurement sensor that transmits short pulses and receives reflected waves from objects in the environment. The main idea of the poposed localization method is to treat the received waveform as a signature of place. The resulting echo waveform is very complex and highly depends on the position of the sensor with respect to surrounding objects. On the other hand, the sensor receives similar waveforms from the same positions. Moreover, the directional characteristics of dipole antenna is almost omnidirectional. Therefore, we can localize the sensor position to find similar waveform from waveform database. This paper proposes a place recognition method based on waveform matching, presents a number of experiments that illustrate the high positon estimation accuracy of our UWB radar-based localization system, and shows the resulting loop detection performance in a typical indoor office environment and a forest.

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“…Rapidly growing computing capabilities and advancements in cheap and robust sensor technology, lead to vision based systems such as, Simultaneous Localization and Mapping (SLAM) [13] and Visual Registration [14], utilizing visual sensors such as camera and LIght Detection and Ranging (LIDAR). Though SLAM and UWB positioning systems are accurate, they suffer from factors such as accumulated errors [15], inability to adapt to environmental conditions, and requirement of an accurate signal propagation model [16]. On the other hand, fiducial markers developed by [14] have the potential to accurately localize the robot's position in an indoor environment (i.e., building floor level and room location), and are also cheap and easy to install [17].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Building Energy and Comfort Parameter Data Collection Using Mobile Indoor Robots

Mantha

Feng²,

Menassa

et al. 2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Optimizing and improving energy performance of buildings while maintaining occupants' comfort are primary goals for building managers. In order to analyze a building's energy performance and make informed retrofit, maintenance, or operational decisions, decision-makers need access to credible real-time data illustrating how building systems are being used by its occupants at a floor and room level granularity. Traditionally, such data has been collected in buildings using wired or wireless systems by installing a dense array of sensors in every building location that needs monitoring. This is an effort and cost-prohibitive approach, especially in existing older buildings where instrumentation and integration with existing building systems is challenging. This paper introduces a novel concept of using autonomous mobile indoor robots for monitoring various occupant comfort and energy parameters inside an existing building, and discusses how the collected data can be utilized in various analyses. The research evaluates the hypothesis that a single multi-sensor fused robotic data mule that collects building energy systems performance and occupancy comfort data at sparse locations inside a building can provide decision-makers with a rich data set that is comparable in fidelity to data obtained from pre-installed and fixed sensor systems. In order to demonstrate the effectiveness of the proposed approach, an experiment was conducted using a tele-operated robot outfitted with thermal comfort data collection sensors and a localization camera in a multi-occupancy space within a large university building. The data collected by the mobile robot was statistically compared with data obtained from the building's pre-installed Building Automation System. Experimental results demonstrated the proposed method's promise and applicability in collecting dense actionable data in large spaces using only a sparse set of sensors mounted on mobile indoor robots.

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Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation

Cited by 29 publications

References 18 publications

Ambient Data Collection in Indoor Building Environments Using Mobile Robots

Ambient Data Collection in Indoor Building Environments Using Mobile Robots

Localization and Place Recognition Using an Ultra-Wide Band (UWB) Radar

Real-Time Building Energy and Comfort Parameter Data Collection Using Mobile Indoor Robots

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