Indoor Localization System

Motivation

For relatively simple autonomous robots, knowing an absolute position in the world frame is a very complex challenge. Many systems attempt to approximate this positioning information using relative measurements from encoders, or local landmarks. Opposed to an absolute system, these relativistic designs are subject to cumulating errors. In this design, the positioning information is calculated by an external computer which then transmits data over a wireless module.

This system can be envisioned as an indoor GPS, where positioning information of known patterns is transmitted over a wireless module available for anyone to read. Unlike a GPS, this vision system has much higher resolution and is designed for indoor use.

Overview of Design

This system uses four standard webcams to locate known patterns in a real time image, and transmit positioning information over a serial interface. This serial interface is most often connected to a wireless Zigbee® module. The cameras are mounted in fixed positions above the target area. The height of the cameras can be adjusted to increase either the positioning resolution or the area of the world frame. These constraints are a function of the field of view of the lenses. Below is a diagram illustrating this system’s basic setup.

Here, we can see the four cameras are mounted rigidly above the world frame. Note that the cameras actual placement must have an overlap along inside edges at least the size of one target. This is necessary to ensure any given target is at least fully inside one camera’s frame.

Goals

• To provide real-time (X, Y, θ) position information to an arbitrary number of targets (<20) in a fixed world frame using a home-made computer vision system.

• Maximize throughput and accuracy

• Minimize latency and noise

• Easy re-calibration of camera poses.

• Reduced cost (as compared to real-time operating systems and frame grabbing technology)

Tools Used

Software

• IDE: Microsoft Visual C++ Express Edition – freeware (http://www.microsoft.com/express/default.aspx)

• Vision Library: Intel OpenCV – open source c++ (http://sourceforge.net/projects/opencvlibrary/)

• Camera Capture Library: VideoInput – open source c++ (http://muonics.net/school/spring05/videoInput/)

Hardware

• Four Logitech QuickCam Communicate Deluxe USB2.0 webcams

• One 4-port USB2.0 Hub

• Computer to run algorithm