Location Estimation using Delayed Measurements

When combining data from various sensors it is vital to acknowledge possible measurement delays. Furthermore, the sensor fusion algorithm, often a Kalman filter, should be modified in order to handle the delay. This paper examines different possibilities for handling delays and applies a new technique to a sensor fusion system for estamating the location of an Autonomous Guided Vehicle. The system fuses encoder and vision measurements in an extended Kalman filter. Results from experiments in a real environment are reported.