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Simultaneous Localization and Mapping (SLAM) is perhaps the most fundamental problem to solve in robotics in order to build truly autonomous mobile robots. The sensors have a large impact on the algorithm used for SLAM. In this work a novel method, called Filtered Inverse Depth Delayed (FIDD) Initialization which is intended for initializing new features in Bearing-Only SLAM systems. Unlike range...
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 maintain a stable computational cost per frame...
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 the position of the robot while it explores...
Cameras have gained a great interest as sensors for the robotic research community, because they yield a lot of information. Cameras are well adapted for embedded systems; they are light, cheap and power saving. As the computational power grows, an inexpensive camera can be used to perform range and appearance-based sensing simultaneously, replacing typical sensors as laser and sonar rings for range...
The ego-motion online estimation process from a video input is often called visual odometry. Typically optical flow and structure from motion (SFM) techniques have been used for visual odometry. Monocular simultaneous localization and mapping (SLAM) techniques implicitly estimate camera ego-motion while incrementally build a map of the environment. However in monocular SLAM, when the number of features...
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