Navigation

In portable navigation, the need to recognize the motion mode of the user, is useful - if not necessary - to improve the positioning estimation. This paper explains the use of sensors in a portable device, such as a smartphone, to recognize the user's mode of motion when a change in height is detected. The modes of motion detected are walking up or down stairs, taking the elevator, and standing or walking on an escalator. The portable device contains an accelerometer triad, gyroscope triad, a barometer, and occasionally a magnetometer triad. The solution is dependent on the sensors, and does not require satellite or wireless positioning. The height motion mode recognition module has been implemented in real-time on several brands of various consumer portable devices, including smartphones, tablets, smartwatches, and smart glasses.

This study proposed the existence of two distinct types of environmental representations: “allocentric-survey” and “egocentric-survey”. The allocentric-survey representation is a top-down (third-person perspective) representation formed as a result of acquiring knowledge of landmarks, routes, and spatial relations between them. In contrast, the egocentric–survey representation is a first-person perspective survey representation formed through an engagement of spatial updating, which pertains to the automatic and continuous updating of transient self-to-object relations as one navigates in space. The results of study 1 suggest that egocentric-survey representations are qualitatively different from allocentric-survey representations since the former preserves information not only about spatial locations, but also about orientation. While both groups were relatively accurate in representing the spatial layout of the route, sketchers of egocentric-survey maps were significantly faster on orientation and navigational pointing judgments than sketchers of allocentric-survey maps. Study 2 designed a Navigational Strategy Questionnaire which included a novel scale assessing a preference for spatial updating navigational strategy in addition to two more traditional scales assessing survey-based and procedural navigational strategies. Critically, the spatial updating scale exhibited predictive validity in relation to large-scale navigational performance and related spatial updating strategy use to the formation of egocentric-survey representations.  

Portable navigation has become increasingly prevalent in daily activities. The need for accurate user positioning information, including a person's location and velocity, when using a portable device (such as a cell phone, tablet, or even a smart watch) is growing in various fields. Knowing the user's mode of motion or conveyance allows appropriate algorithms or constraints, related to each mode, to be used to estimate a more accurate position and velocity. The modes covered in this paper are walking, running, cycling, and land-based vessels (including vehicles, truck, buses, and trains which include light rail trains and subways). The work discussed in this paper involves the use of sensors — with and without Global Navigation Satellite Systems (GNSS) signal availability —in portable devices to help recognize the mode of motion for an arbitrary user, an arbitrary use case — whether the device is held in the hand, in the pocket, or at the ear, etc. — and an arbitrary orientation of the device.

This paper presents a new algorithm for underwater geomagnetic-matching localization. The idea is rooted from image registration and workpiece localization problem. One essential elements of the new algorithm is an algorithm to solve transformation between two point sets. A related problem to this is fitting of two point sets (FTPS). In this paper, FTPS is first introduced and formulated to a least-squares problem. Then four algorithms used to solve FTPS are introduced. Another essential element of the new algorithm is iteration. The iteration method in ICCP is adopted. Based on above two aspects, the main steps of the new method are given. Simulation experiments are designed to evaluate the performance of the new algorithm. The results show that the new algorithm can solve geomagnetic-matching localization satisfactorily.