pypersonnelloc

pypersonnelloc is personnel localization service to extract estimate of position coordinate in a noisy indoor environment.

Following algorithm are supported:

• Robust Adaptive Kalman Filter

Development

Python3.x

1. Create a Virtual Environment

   $ virtualenv -m venv venv

2. Activate Virtual Environment

   $ . venv/bin/activate 

3. Install the Dependencies

   $ pip install -r requirements.txt

4. Install pypersonnelloc as python package for development:

   $ pip install -e .

   This makes the personnel-localization binary available as a CLI

Usage

Run personnel-localization binary in command line:

• -c : Configuration file path
• -i : ID of the personnel
• -s : 2D/3D start Coordinates of the personnel (Initial/start point)

$ personnel-localization -c config.yaml -i 1 -s 10 20

Message Broker (RabbitMQ)

Use the rabbitmqtt stack for the Message Broker

NOTE: The rabbitmqtt stack needs an external docker network called iotstack make sure to create one using docker network create iotstack

Docker

1. To build Docker Images locally use:

   $ docker build -t pypersonnelloc:<version> .

2. To run the Application along with the RabbitMQ Broker connect the container with the iotstack network using:

   $ docker run --rm --network=iotstack -t pypersonnelloc:<version> -c config.yaml -i 1 -s 10 20

   INFO: Change the broker address in the config.yaml file to rabbitmq (name of the RabbitMQ Container in rabbitmqtt stack)

3. To run the a custom configuration for the Container use:

   $ docker run --rm -v $(pwd)/config.yaml:/pypersonnelloc/config.yaml --network=iotstack -t pypersonnelloc:<version> -c config.yaml -i 1 -s 10 20

Reference Paper

1. Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

   https://doi.org/10.3390/s18061970

Maintainers

The repository is maintained by:

• Karthik Shenoy Panambur
• Shantanoo Desai

BIBA - Bremer Institut für Produktion und Logistik GmbH

FUNDING

• The development of this codebase and repository is driven through the RAINBOW Project. RAINBOW Project has received funding from the European Union’s Horizon 2020 programme under grant agreement number 871403
• The development of this codebase and repository is driven through the ASSURED Project. ASSURED project is funded by the European Union's Horizon 2020 programme under Grant Agreement number 952697