Elevator and Escalator Classification for Precise Indoor Localization

Kronenwett, Nikolai; Song, Qian; Mueller, K.; Trommer, Gert F.

doi:10.1109/ipin.2018.8533804

Cited by 9 publications

(16 citation statements)

References 10 publications

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“…In contrast, horizontal ZUPTs and delta ZUPTs are applied during stance phases using an lift. Absolute velocity measurements are generated during stance phases on an escalator [48]. If GNSS pseudo-range and Doppler measurements are available, the kind of aiding depends on the current classified motion state.…”

Section: ) Team Kitmentioning

confidence: 99%

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The IPIN 2019 Indoor Localisation Competition—Description and Results

Potortì¹,

Park²,

Crivello³

et al. 2020

IEEE Access

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Section: ) Team Kitmentioning

confidence: 99%

“…The approach is able to detect and separate lift from escalator movements [48]. In addition, the lift and escalator rides are subdivided in different sub-states like acceleration phase, constant velocity phase and braking phase.…”

Section: B: Lift and Escalator Detectormentioning

confidence: 99%

The IPIN 2019 Indoor Localisation Competition—Description and Results

Potortì¹,

Park²,

Crivello³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

“…For instance, in the so called SemanticSLAM algorithm [12] the escalators and elevators are detected via a binary decision tree, which was also proposed for classification by [24]. A state machine was used in [25]. The authors of [26] tracked the pattern of the elevator ride by integrating the z-acceleration.…”

Section: Introductionmentioning

confidence: 99%

“…A combination of consulting the integrated z-acceleration, the disturbances of the earth magnetic field and the barometric signal was proposed in [27]. Another acceleration phase tracking based on the detection of the acceleration and deceleration peaks was proposed in [12,25,28], where in [25,28] the velocity is corrected during the constant velocity phase.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation

Kaiser

Lang

2018

Sensors

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In 3D pedestrian indoor navigation applications, position estimation based on inertial measurement units (IMUss) fails when moving platforms (MPs), such as escalators and elevators, are not properly implemented. In this work, we integrate the MPs in an upper 3D-simultaneous localization and mapping (SLAM) algorithm which is cascaded to the pedestrian dead-reckoning (PDR) technique. The step and heading measurements resulting from the PDR are fed to the SLAM that additionally estimates a map of the environment during the walk in order to reduce the remaining drift. For integrating MPs, we present a new proposal function for the particle filter implementation of the SLAM to account for the presence of MPs. In addition, a new weighting function for features such as escalators and elevators is developed and the features are learned and stored in the learned map. With this, locations of MPs are favored when revisiting the MPs again. The results show that the mean height error is about 0.1 m and the mean position error is less than 1 m for walks with long distances along the floors, even when using multiple floor level changes with different numbers of floors in a multistory environment. For walks with short walking distances and many floor level changes, the mean height error can be higher (about 0.5 m). The final floor number is in all cases except one correctly estimated.

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Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

Renaudin¹,

Ortiz²,

Perul³

et al. 2019

IEEE Access

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The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75 th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future.

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Elevator and Escalator Classification for Precise Indoor Localization

Cited by 9 publications

References 10 publications

The IPIN 2019 Indoor Localisation Competition—Description and Results

The IPIN 2019 Indoor Localisation Competition—Description and Results

Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation

Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

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