SLAM using camera and IMU sensors. | Zendy

Fredrick Rothganger | Zendy; Maritza Muguira | Zendy

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SLAM using camera and IMU sensors.

Author(s) -

Fredrick Rothganger,

Maritza Muguira

Publication year - 2007

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/961651

Subject(s) - inertial measurement unit , computer vision , artificial intelligence , simultaneous localization and mapping , computer science , kalman filter , extended kalman filter , process (computing) , path (computing) , mobile robot , robot , operating system , programming language

Visual simultaneous localization and mapping (VSLAM) is the problem of using video input to reconstruct the 3D world and the path of the camera in an 'on-line' manner. Since the data is processed in real time, one does not have access to all of the data at once. (Contrast this with structure from motion (SFM), which is usually formulated as an 'off-line' process on all the data seen, and is not time dependent.) A VSLAM solution is useful for mobile robot navigation or as an assistant for humans exploring an unknown environment. This report documents the design and implementation of a VSLAM system that consists of a small inertial measurement unit (IMU) and camera. The approach is based on a modified Extended Kalman Filter. This research was performed under a Laboratory Directed Research and Development (LDRD) effort

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