LoCo

Highly accurate location framework

LoCo is a framework that supports highly accurate room-level location sensing.

In recent years, there has been an explosion of social and collaborative applications that leverage location to provide users novel and engaging experiences. However, many applications fail to realize their full potential because of limitations in current location technologies. Current frameworks work well outdoors but fare poorly indoors.

To address this limitation, we have developed LoCo, a new framework that can provide highly accurate room-level location. A strength of the framework is its ability to perform localization using many different underlying technologies. For instance, to position everyday smart devices, like iPhones and iPads, Loco can leverage the device’s Bluetooth Low-Energy radio, combined with a constellation of low-cost beacons deployed in an environment. For more sophisticated positioning, LoCo can combine additional sensors (e.g. IMU) and contextual information about a location (e.g. map data) to estimate a more accurate location. LoCo’s estimation techniques and algorithms can also use more sophisticated ranging technologies, like Ultra-Wideband (UWB) and WiFi RTT. LoCo is also able to leverage many different sensors and radios to perform location positioning. This allows the same SDKs and cloud services to be used across a variety of application domains and environments.

We are leveraging LoCo to enable many new services, especially those that support collaboration. For instance, LoCo is being used to enhance existing communication (e.g. Skype) and presence systems (such as MyUnity) to provide more precise understanding of availability. Further, LoCo can be used as a framework to study collaborative behavior in settings like the workplace without imposing a heavy burden on users (from their perspective, it is simply an app on their smartphone.)

Related Publications

2018
Publication Details
  • International Conference on Indoor Positioning and Indoor Navigation
  • Sep 9, 2018

Abstract

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Accurate localization is a fundamental requirement for a variety of applications, ranging from industrial robot operations to location-powered applications on mobile devices. A key technical challenge in achieving this goal is providing a clean and reliable estimation of location from a variety of low-cost, uncalibrated sesnors. Many current techniques rely on Particle Filter (PF) based algorithms. They have proven successful at effectively fusing various sensors inputs to create meaningful location predictions. In this paper we build upon this large corpous of work. Like prior work, our technique fuses Received Signal Strength Indicator (RSSI) measurements from Bluetooth Low Energy (BLE) beacons with map information. A key contribution of our work is a new sensor model for BLE beacons that does not require the mapping from RSSI to distance. We further contribute a novel method of utilizing map information during the initialization of the system and during the resampling phase when new particles are generated. Using our proposed sensor model and map prior information the performance of the overall localization is improved by 1.20 m on comparing the 75th percentile of the cumulative distribution with traditional localization techniques.
Publication Details
  • 9th International Conference on Indoor Positioning and Indoor Navigation
  • Sep 9, 2018

Abstract

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In this paper, we develop a system for the lowcost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors. We develop a novel and simplified probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the robot trajectory. Our algorithm can globally localize and track a robot with a priori unknown location, given an informative prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the problem as an optimization problem that serves as the Backend of the algorithm mentioned above (Front-end). Thus, by simultaneously solving for the robot trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the robot, corrected locations of the BLE beacons, and the time varying IMU bias. The evaluations achieved using hardware show that through the proposed closed-loop system the localization performance can be improved; furthermore, the system becomes robust to the error in the map of beacons by feeding back the optimized map to the Front-end.
2016
Publication Details
  • Personal and Ubiquitous Computing (Springer)
  • Feb 19, 2016

Abstract

Close
In recent years, there has been an explosion of services that lever- age location to provide users novel and engaging experiences. However, many applications fail to realize their full potential because of limitations in current location technologies. Current frameworks work well outdoors but fare poorly indoors. In this paper we present LoCo, a new framework that can provide highly accurate room-level indoor location. LoCo does not require users to carry specialized location hardware—it uses radios that are present in most contemporary devices and, combined with a boosting classification technique, provides a significant runtime performance improvement. We provide experiments that show the combined radio technique can achieve accuracy that improves on current state-of-the-art Wi-Fi only techniques. LoCo is designed to be easily deployed within an environment and readily leveraged by application developers. We believe LoCo’s high accuracy and accessibility can drive a new wave of location-driven applications and services.
2014
Publication Details
  • Ubicomp 2014
  • Sep 9, 2014

Abstract

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In recent years, there has been an explosion of social and collaborative applications that leverage location to provide users novel and engaging experiences. Current location technologies work well outdoors but fare poorly indoors. In this paper we present LoCo, a new framework that can provide highly accurate room-level location using a supervised classification scheme. We provide experiments that show this technique is orders of magnitude more efficient than current state-of-the-art Wi- Fi localization techniques. Low classification overhead and computational footprint make classification practical and efficient even on mobile devices. Our framework has also been designed to be easily deployed and lever- aged by developers to help create a new wave of location- driven applications and services.