Accurate height estimation based on apriori knowledge of buildings

For Search and Rescue (SAR) applications in large buildings, vertical accuracy may seem to be more important than horizontal accuracy, because correctness of floor estimation is mission critical. In this paper, an approachfor height error suppression based on apriori knowledge is proposed, which focuses on long-term height drift suppression along with proper floor determination. An Extended Kalman Filter (EKF) algorithm is firstly reviewed, which integrates barometer data to correct height drift brought by gyroscopes and accelerometers. However, height drift caused by barometer is still unacceptable in a long-term duration. So a new method is proposed under the assumption that a person's height only changes on stairs in the building. Based on this assumption, the whole course of walking in a building is partitioned into floor phase and stair phase, according to a built HMM model. Height estimation can be corrected by considering apriori knowledge of buildings, and will be performed following different rules in the two phases. For the floor phase, the height remains consistent on the same floor, and minor drifts are suppressed inspired by Heuristic Drift Elimination (HDE). For the stair phase, after sufficient stair-related information is acquired, a Maximum Likelihood Estimation (MLE) method is used to estimate the height of each stair step, and drift-free height difference between two floors can be estimated using the estimated stair step height. The experimental results of the proposed approach demonstrate the effectiveness of the approach for height error suppression in SAR use with forced ventilation and sudden change in air pressure.