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This paper describes a practical and reliable algorithm for implementing an Attitude and Heading Reference System (AHRS). This kind of system is essential for real time vehicle navigation, guidance and control applications. When low cost sensors are used, efficient and robust algorithms are required for performance to be acceptable. The proposed method is(More)
—Monocular simultaneous localization and mapping (SLAM) techniques implicitly estimate camera ego-motion while incrementally building a map of the environment. In monocular SLAM, when the number of features in the system state increases, maintaining a real-time operation becomes very difficult. However , it is easy to remove old features from the state to(More)
—Recently, the unified inverse depth parametrization has shown to be a good option for challenging monocular SLAM problem, in a scheme of EKF for the estimation of the stochastic map and camera pose. In the original approach, features are initialized in the first frame observed (undelayed initialization), this aspect has advantages but also some problems.(More)
In this paper we present a method to recognize images features with a wide base line between learning and recognition phases. The method is based in feature descriptors derived from independent component analysis (ICA). This technique is inspired by the problems of mobile robot mapping and localization using single camera. In the learning phase the(More)
This work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are introduced taking advantage of data from a secondary monocular(More)
Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case,(More)
The 6-DOF monocular camera case possibly represents the harder variant in the context of simultaneous localization and mapping problem. In the last years, several advances have been appeared in this area; however the application of these techniques to real world applications it's difficult so far. Recently, the unified inverse depth parametrization has(More)
The on-line robot estimation position from measurements of self-mapped features is a class of problem called, in the robotics community, as simultaneous localization and mapping (SLAM) problem, which is one of the fundamental problems in robotics. SLAM consists in incrementally building a consistent map of the environment and, at the same time, localizing(More)
The present paper describes a methodology to estimate the attitude and the position of a bio-inspired robot as well the position of a target. The robot is equipped with a decoupled eye yielding an angular measurement relative to a target and it does not use an IMU. An Extended Kalman Filter is designed to estimate robot and target states. Simulation results(More)