Location Detection for Navigation Using IMUs with a Map Through Coarse-Grained Machine Learning

J. Jose Gonzalez E.1,a, Chen Luo1,b, Anshumali Shrivastava1,c, Krishna Palem1,d, Yongshik Moon2,e, Soonhyun Noh2,f, Daedong Park2,g and Seongsoo Hong2,h
1Rice University, Houston, Texas, USA.
2Seoul National University, Seoul, Korea.


Location detection or localization supporting navigation has assumed significant importance in the recent past. In particular, techniques that exploit cheap inertial measurement units (IMU), the gyroscope and the accelerometer, have garnered attention, especially in an embedded computing context. However, these sensors measurements are quite unreliable, and it is widely believed that these sensors by themselves are too noisy for localization with acceptable accuracy. Consequently, several lines of work embody other costly alternatives to lower the impact of accumulated errors associated with IMU based approaches, invariably leading to very high energy costs resulting in lowered battery life. In this paper, we show that IMUs are sufficient by themselves if we augment them with known structural or geographical information about the physical area being explored by the user. By using the map of the region being explored and the fact that humans typically walk in a structured manner, our approach sidesteps the challenges created by noise and concomitant accumulation of error. Specifically, we show that a simple coarse-grained machine learning approach mitigates the effect of the noisy perturbations in the information from our IMUs, provided we have accurate maps. Throughout, we rely on the principle of inexactness in an overarching manner and relax the need for absolute accuracy in return for significant lowering of resource (energy) costs. Notably, our approach is completely independent of any external guidance from sources including GPS, Bluetooth or WiFi support, and is this privacy preserving. Specifically, we show through experimental results that by relying on gyroscope and accelerometer data alone, we can correctly identify the path-segment where the user is walking/running on a known map, as well as the position within the path with an accuracy of 4.3 meters on the average using 0.44 Joules. This is a factor of 27X cheaper in energy lower than the ``gold standard'' that one could consider based on GPS support which, surprisingly, has an associated error of 8.7 meters on the average.

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