Faithin Omaha eBook Collection

Download or read book State Estimation Solely Based on Prior Knowledge and Inertial Sensors written by Tom Lucas Koller and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: How do we localize ourselves? Ever since GPS exists, it is common to know where we are and how to get to our desired location. Unfortunately, GPS is unavailable indoors. Scientists are looking for an alternative technology that can fill this localization gap. One approach is to fuse knowledge about our environment and our movement measured with inertial sensors. A particular difficulty of these sensors is that their pose (position and orientation) estimation error grows over time. This so-called drift can lead to false estimations such as passing through a wall. These false estimations could be corrected by using prior knowledge of the wall's location. In this work, I investigate how prior knowledge can be fused with inertial sensor measurements. The practical aim of this thesis is to eliminate the drift without additional sensors. I investigate three types of prior knowledge regarding the environment and the movement: The human gait pattern, terrain maps, and event-domain maps. For all three types, I follow the concept of modeling the prior knowledge as probability distributions of the system's state. This modeling enables the usage of standard probability-based algorithms to estimate the position and orientation and to fuse the knowledge with sensor measurements. The human gait is an alternating pattern of stance and swing phases. I show a new approach based on the Interacting Multiple Model Filter that can detect the phase and improve the velocity estimate of the inertial sensor. The approach automatically detects whether the sensor measurements match the probability distribution of the stance or swing phase. Simultaneously, it corrects the measurement errors of the inertial sensor by taking into account the probability distributions. The evaluation shows the potential of this method, albeit further development is required to outperform state of the art approaches. Terrain maps define the height of a vehicle or a human given its position in the horizontal plane. This can be modeled as a so-called pseudo measurement. We act like there is a sensor that measures the height above the surface but always returns zero since there is no height difference. In this way, a probability distribution is modeled that constrains the position to the surface. I investigate terrain maps with the practical example of track cycling. I show that terrain maps can yield full observability of the position and orientation; in other words, that they are able to correct the growing error of the inertial sensor. Thereby, only the curved parts of the track yield information about the position. As a result, the position can be tracked during 10km drives with an error of 1:08m (RMSE). Event-domain maps are a particular type of maps that specify where activities can be performed. For example, it is only possible to climb stairs at staircases. I investigate this type of knowledge at bouldering, where the climbers grip the holds of a route. The map represents a probability distribution of possible grip positions. I develop a two-step method where the first step estimates the transition between two holds. In a second step, the transitions are refined using the event-domain map. The estimated error improves from 0:266m (median) to 0:132m compared to an integrating solution without a map. Overall, modeling the three types of prior knowledge successfully reduces the drift in all cases. The human gait pattern can be utilized with a new kind of state estimator, which needs further investigation. The map-based types of knowledge correct the drift of the inertial sensor in the experiments. For the terrain map, it is even possible to prove the correction mathematically. This shows that prior knowledge modeled as prior distribution is effective to estimate the position solely with inertial sensors.