Abstract-The Simultaneous Localization And Mapping (SLAM) estimation problem is a nonlinear problem, due to the nature of the range and bearing measurements. In latter years it has been demonstrated that if the nonlinearities from the attitude are handled by a separate nonlinear observer, the SLAM dynamics can be represented as a linear time varying (LTV) system, by introducing these nonlinearities and nonlinear measurements as time varying vectors and matrices. This makes the SLAM estimation problem globally solvable with a Kalman filter, however, the noise structure is no longer trivial. In this paper, a new bearings only SLAM estimation algorithm is presented, including a novel design of the noise covariance matrices. Simulations of the SLAM estimator are presented, and show the performance of the state and uncertainty estimates, as well as the stability of the proposed estimator.

For some problems, such as monocular visual odometry (VO), vector measurements are given with unknown magnitude. In VO, the magnitude can be found by recognizing features with known position, or with an extra sensor such as an altimeter. This article presents a nonlinear observer that uses the derivative of the vector as an additional measurement for estimating the magnitude of a vector. For the VO example, this means that the velocity can be estimated by fusing the normalized velocity vector with acceleration measurements. The observer exploits the fact that the dynamics of the normalized vector is dependent on the magnitude of the vector. The observer employs methods from nonlinear/adaptive estimation; filters the unit vector on the unit sphere, and retrieves the magnitude of the vector. The observer is shown to be uniformly semi-globally asymptotically (USGAS) stable and uniformly exponentially stable (UES) in a defined region. The observer is applied to the bearing-only SLAM filter problem as an example.

The problem of estimating velocity from a monocular camera and calibrated inertial measurement unit (IMU) measurements is revisited. For the presented setup, it is assumed that normalized velocity measurements are available from the camera. By applying results from nonlinear observer theory, we present velocity estimators with proven global stability under defined conditions, and without the need to observe features from several camera frames. Several nonlinear methods are compared with each other, also against an extended Kalman filter (EKF), where the robustness of the nonlinear methods compared with the EKF are demonstrated in simulations and experiments.

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In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms.