Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
IEEE Computer Graphics and Applications
Pedestrian localisation for indoor environments
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
User experiences with activity-based navigation on mobile devices
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
PINwI: pedestrian indoor navigation without infrastructure
Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries
FindingMiMo: tracing a missing mobile phone using daily observations
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Human navigation and mapping with a 6DOF IMU and a laser scanner
Robotics and Autonomous Systems
Zee: zero-effort crowdsourcing for indoor localization
Proceedings of the 18th annual international conference on Mobile computing and networking
Improving indoor localization with social interactions
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Smartphone-based pedestrian tracking in indoor corridor environments
Personal and Ubiquitous Computing
Walkie-Markie: indoor pathway mapping made easy
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Social-Loc: improving indoor localization with social sensing
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Smartphone-based indoor pedestrian tracking using geo-magnetic observations
Mobile Information Systems
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In this paper we describe a new Bayesian estimation approach for simultaneous mapping and localization for pedestrians based on odometry with foot mounted inertial sensors. When somebody walks within a constrained area such as a building, then even noisy and drift-prone odometry measurements can give us information about features like turns, doors, and walls, which we can use to build a form of a map of the explored area, especially when these features are revisited over time. Our initial results for our novel scheme which we call "FootSLAM" are very surprising in that true SLAM with stable relative positioning accuracy of 1-2 meters for pedestrians is indeed possible based on inertial sensors alone without any prior known building indoor layout. Furthermore, the 2D maps obtained even for just 10 minutes of walking converge to a good approximation of the true layout forming the basis for future automated collaborative mapping of buildings.