Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors

Abstract

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.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…FootSLAM uses a Bayesian approach, where the state is the user's pose and step measurements allow updating both user trajectory and environment map. It is implemented as a Rao-Blackwellized Particle Filter (RBPF), where each particle is composed of a user trajectory instance and its related map [184]. On the other hand, PlaceSLAM assumes proximity information relative to some well recognizable places, e.g.…”

RollCaller: User-friendly indoor navigation system using human-item spatial relation

“…(4) Smart phone, equipped with accelerometer and magnetometer and running a RollCaller client connecting with the back-end server. It is carried by the user to measure the displacement of himself using IMU-based displacement measurement [12], [13], [24]. Also, it acts as an interface to acquire the information of the item the user wants, and remind the user to stop to take that item.…”

Montage: Combine frames with movement continuity for realtime multi-user tracking

“…Most of them use step-and-heading-based dead-reckoning, e.g. [21],with special devices and absolute position fixes are required to correct dead-reckoning output. Some work use the inertial sensors of smartphones with indoor maps to track users as they traverse indoor, e.g., [20].…”

“…The movement continuity may also help to remove ambiguities from each frame. Previous schemes estimate the moving distance and direction by dead-reckoning [21]. But special devices or pre-knowledge are usually required, e.g., [20], [27], and absolute positions are also require to fix the accumulated errors.…”