Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation

Kaiser, Susanna; Lang, Christopher

doi:10.3390/s18124367

Cited by 4 publications

(2 citation statements)

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“…, where N is the number of particles. Extensions of the FootSLAM algorithm have considered estimation of systematic odometry errors [19], navigation and mapping in three dimensions [20], collaborative mapping [21], fusion with magnetic field measurements [22] and user provided hints [23], and navigation in the presence of moving platforms such as escalators and elevators [24].…”

Section: Footslammentioning

confidence: 99%

FootSLAM meets Adaptive Thresholding

2020

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Calibration of the zero-velocity detection threshold is an essential prerequisite for zero-velocity-aided inertial navigation. However, the literature is lacking a self-contained calibration method, suitable for large-scale use in unprepared environments without map information or pre-deployed infrastructure. In this paper, the calibration of the zero-velocity detection threshold is formulated as a maximum likelihood problem. The likelihood function is approximated using estimation quantities readily available from the FootSLAM algorithm. Thus, we obtain a method for adaptive thresholding that does not require map information, measurements from supplementary sensors, or user input. Experimental evaluations are conducted using data with different gait speeds, sensor placements, and walking trajectories. The proposed calibration method is shown to outperform fixedthreshold zero-velocity detectors.

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Section: Footslammentioning

confidence: 99%

FootSLAM meets Adaptive Thresholding

2020

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“…An example of landmarks used to correct position error are corridors, corners and stairs [ 15 , 16 , 17 ]. The authors in [ 18 ] detect escalators and lifts, which can be used as well as landmarks.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Localization for Passengers Commuting in Traffic Hubs

Romero

Diaz

Ahmed

2022

Sensors

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Passengers commute between different modes of transportation in traffic hubs, and passenger localization is a key component for the effective functioning of these spaces. The smartphone-based localization system presented in this work is based on the 3D step and heading approach, which is adapted depending on the position of the smartphone, i.e., held in the hand or in the front pocket of the trousers. We use the accelerometer, gyroscope and barometer embedded in the smartphone to detect the steps and the direction of movement of the passenger. To correct the accumulated error, we detect landmarks, particularly staircases and elevators. To test our localization algorithm, we have recorded real-world mobility data in a test station in Munich city center where we have ground truth points. We achieve a 3D position accuracy of 12 m for a smartphone held in the hand and 10 m when the phone is placed in the front pocket of the trousers.

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