A Novel Lightweight Particle Filter for Indoor Localization

Pipelidis, Georgios; Tsiamitros, Nikolaos; Gentner, Christian; Ahmed, Dina Bousdar; Prehofer, Christian

doi:10.1109/ipin.2019.8911744

Cited by 14 publications

(15 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detailed floor plans of public buildings are not always available. Reference [ 24 ] used the simplified Open Street Maps (OSM) floor plans and enhanced them, assuming interior properties (e.g., minimum corridor width) conformed to established standards. A mesh-based transition model was proposed in [ 23 ], which allowed propagation towards possible locations only by calculating all wall intersections once in an offline phase.…”

Section: Related Workmentioning

confidence: 99%

“…What makes map matching especially powerful is that it can be used when the initial position is unknown. The initial particles are then uniformly distributed over the floor plan [ 23 , 24 ]. When the initial heading is unknown, the particles are initialized with a random heading [ 20 , 23 ] or the magnetometer is used to estimate the initial heading [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…The output of a PDR algorithm is often matched to a scaled floor plan by a Particle Filter (PF) [ 20 , 21 , 22 , 23 , 24 ]. Wall locations are used as physical constraints, eliminating drift caused by both heading and step length estimation errors.…”

Section: Introductionmentioning

confidence: 99%

“…Wall locations are used as physical constraints, eliminating drift caused by both heading and step length estimation errors. In fact, the PF with map matching can track the pedestrian even if the initial position and/or heading are unknown [ 20 , 23 , 24 ]. Common problems with the PDR-PF approach are sample impoverishment [ 25 ] and multimodality [ 19 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Another challenge in an indoor pedestrian localisation system is detecting floor transitions and determining the correct floor number (i.e., floor detection), thus allowing a pedestrian to be tracked across multiple floors. This can be achieved by detecting the floor number directly using (WiFi) fingerprinting [ 25 , 27 ] or by detecting floor transition events using data from onboard sensors [ 24 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Cock

Joseph

Martens

et al. 2021

Sensors

View full text Add to dashboard Cite

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%