TY - GEN
T1 - Enhancing Indoor Smartphone Location Acquisition Using Floor Plans
AU - Rajagopal, Niranjini
AU - Lazik, Patrick
AU - Pereira, Nuno
AU - Chayapathy, Sindhura
AU - Sinopoli, Bruno
AU - Rowe, Anthony
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - Indoor localization systems typically determine a position using either ranging measurements, inertial sensors, environmental-specific signatures or some combination of all of these methods. Given a floor plan, inertial and signature-based systems can converge on accurate locations by slowly pruning away inconsistent states as a user walks through the space. In contrast, range-based systems are capable of instantly acquiring locations, but they rely on densely deployed beacons and suffer from inaccurate range measurements given non-line-of-sight (NLOS) signals. In order to get the best of both worlds, we present an approach that systematically exploits the geometry information derived from building floor plans to directly improve location acquisition in range-based systems. Our solving approach can disambiguate multiple feasible locations taking into account a mix of LOS and NLOS hypotheses to accurately localize with significantly fewer beacons. We demonstrate our geometry-aware solving approach using a new ultrasonic beacon platform that is able to perform direct time-of-flight ranges on commodity smartphones. The platform uses Bluetooth Low Energy (BLE) for time synchronization and ultrasound for measuring propagation distance. We evaluate our system's accuracy with multiple deployments in a university campus and show that our approach shifts the 80% accuracy point from 4-8m to 1m as compared to solvers that do not use the floor plan information. We are able to detect and remove NLOS signals with 91.5% accuracy.
AB - Indoor localization systems typically determine a position using either ranging measurements, inertial sensors, environmental-specific signatures or some combination of all of these methods. Given a floor plan, inertial and signature-based systems can converge on accurate locations by slowly pruning away inconsistent states as a user walks through the space. In contrast, range-based systems are capable of instantly acquiring locations, but they rely on densely deployed beacons and suffer from inaccurate range measurements given non-line-of-sight (NLOS) signals. In order to get the best of both worlds, we present an approach that systematically exploits the geometry information derived from building floor plans to directly improve location acquisition in range-based systems. Our solving approach can disambiguate multiple feasible locations taking into account a mix of LOS and NLOS hypotheses to accurately localize with significantly fewer beacons. We demonstrate our geometry-aware solving approach using a new ultrasonic beacon platform that is able to perform direct time-of-flight ranges on commodity smartphones. The platform uses Bluetooth Low Energy (BLE) for time synchronization and ultrasound for measuring propagation distance. We evaluate our system's accuracy with multiple deployments in a university campus and show that our approach shifts the 80% accuracy point from 4-8m to 1m as compared to solvers that do not use the floor plan information. We are able to detect and remove NLOS signals with 91.5% accuracy.
KW - floor plan integration
KW - indoor localization
KW - location acquisition
KW - NLOC detection
KW - range based localization
KW - ray tracing
UR - https://www.scopus.com/pages/publications/85056270365
U2 - 10.1109/IPSN.2018.00056
DO - 10.1109/IPSN.2018.00056
M3 - Conference contribution
AN - SCOPUS:85056270365
T3 - Proceedings - 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2018
SP - 278
EP - 289
BT - Proceedings - 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2018
Y2 - 11 April 2018 through 13 April 2018
ER -