Journal ArticleDOI
Pedestrian tracking with shoe-mounted inertial sensors
TLDR
The NavShoe device provides not only robust approximate position, but also an extremely accurate orientation tracker on the foot, which can greatly reduce the database search space for computer vision, making it much simpler and more robust.Abstract:
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.read more
Citations
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Proceedings ArticleDOI
Sensor fusion for flexible human-portable building-scale mapping
TL;DR: A system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors achieves real-time performance in indoor environments of moderate scale.
Journal ArticleDOI
AbolDeepIO: A Novel Deep Inertial Odometry Network for Autonomous Vehicles
TL;DR: This paper presents a novel triple-channel deep IO network architecture based on the physical and mathematical models of IMUs that outperforms all the existing solutions on the IMU readings of the challenging Micro Aerial Vehicle dataset and improves the accuracy by approximately 25%.
SmartSLAM - An Efficient Smartphone Indoor Positioning System Exploiting Machine Learning and Opportunistic Sensing
Ramsey Faragher,Robert Harle +1 more
TL;DR: SmartSLAM moves between different sensor fusion algorithms depending on the current level of certainty in the system, reducing the computational load of the tracking engine, maintaining good positioning performance, improving battery life and freeing CPU cycles for foreground processes.
Journal ArticleDOI
Monitoring Motor Symptoms During Activities of Daily Living in Individuals With Parkinson's Disease.
TL;DR: Wearable sensor systems to monitor motor symptoms in individuals with Parkinson's disease (PD) during activities of daily living (ADLs) appear to be the most promising way to detect symptoms using a small number of sensors such as neural networks.
Proceedings ArticleDOI
LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation
Brandon Wagstaff,Jonathan Kelly +1 more
TL;DR: This work presents a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero- Velocity detector with a long short-term memory (LSTM) neural network, and demonstrates how this LSTM-based zero-VELocity detector operates effectively during crawling and ladder climbing.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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TL;DR: Point Research Corporation of Santa Ana, CA has developed a new lightweight miniature Dead Reckoning Module (DRM) for drift-free navigation by personnel on foot as discussed by the authors, which is protected under U.S. patent number 5,583,776.