Modern smartphones have all the sensing capabilities required for accurate and robust navigation and tracking. In specific environments some data streams may be absent, less reliable, or flat out wrong. In particular, the GNSS signal can become flawed or silent inside buildings or in streets with tall buildings. In this application paper, we aim to advance the current state-of-the-art in motion estimation using inertial measurements in combination with partial GNSS data on standard smartphones. We show how iterative estimation methods help refine the positioning path estimates in retrospective use cases that can cover both fixed-interval and fixed-lag scenarios. We compare estimation results provided by global iterated Kalman filtering methods to those of a visual-inertial tracking scheme (Apple ARKit). The practical applicability is demonstrated on real-world use cases on empirical data acquired from both smartphones and tablet devices.
Besides SHS and VIO approaches, there are also pure inertial navigation approaches which estimate the full motion trajectory in 3D by using foot-mounted consumer-grade inertial sensors @cite_28 @cite_0 . With foot-mounted sensors the inertial navigation problem is considerably easier than in the general case since the drift can be constrained by using zero-velocity updates, which are detected on each step when the foot touches the ground and the sensor is stationary. However, automatic zero-velocity updates are not applicable for handheld or flying devices, and the approach is not suitable to large-scale consumer use since the current solutions do not work well when the movement happens without steps ( , in a trolley or escalator). In addition, the type of shoes and sensor placement in the foot may affect the robustness and accuracy of estimation. A prominent example in this class of approaches is the OpenShoe project @cite_0 @cite_2 , which actually uses several pairs of accelerometers and gyroscopes to estimate the step-by-step PDR.
@cite_28 @cite_0 @cite_2
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A navigation system that tracks the location of a person on foot is useful for finding and rescuing firefighters or other emergency first responders, or for location-aware computing, personal navigation assistance, mobile 3D audio, and mixed or augmented reality applications. One of the main obstacles to the real-world deployment of location-sensitive wearable computing, including mixed reality (MR), is that current position-tracking technologies require an instrumented, marked, or premapped environment. At InterSense, we've developed a system called NavShoe, which uses a new approach to position tracking based on inertial sensing. Our wireless inertial sensor is small enough to easily tuck into the shoelaces, and sufficiently low power to run all day on a small battery. Although it can't be used alone for precise registration of close-range objects, in outdoor applications augmenting distant objects, a user would barely notice the NavShoe's meter-level error combined with any error in the head's assumed location relative to the foot. NavShoe can greatly reduce the database search space for computer vision, making it much simpler and more robust. The NavShoe device provides not only robust approximate position, but also an extremely accurate orientation tracker on the foot. Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.