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9 May 2009 Integration of 3D and 2D imaging data for assured navigation in unknown environments: initial steps
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This paper discusses the initial steps of the development of a novel navigation method that integrates three-dimensional (3D) point cloud data, two-dimensional (2D) gray-level (intensity), and data from an Inertial Measurement Unit (IMU). A time-of-flight camera such as MESA's Swissranger will output both the 3D and 2D data. The target application is position and attitude determination of unmanned aerial vehicles (UAV) and autonomous ground vehicles (AGV) in urban or indoor environments. In urban and indoor environments a GPS position capability may not only be unavailable due to shadowing, significant signal attenuation or multipath, but also due to intentional denial or deception. The proposed algorithm extracts key features such as planar surfaces, lines and corner-points from both the 3D (point-cloud) and 2D (intensity) imagery. Consecutive observations of corresponding features in the 3D and 2D image frames are then used to compute estimates of position and orientation changes. Since the use of 3D image features for positioning suffers from limited feature observability resulting in deteriorated position accuracies, and the 2D imagery suffers from an unknown depth when estimating the pose from consecutive image frames, it is expected that the integration of both data sets will alleviate the problems with the individual methods resulting in an position and attitude determination method with a high level of assurance. An Inertial Measurement Unit (IMU) is used to set up the tracking gates necessary to perform data association of the features in consecutive frames. Finally, the position and orientation change estimates can be used to correct for the IMU drift errors.
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Evan Dill and Maarten Uijt de Haag "Integration of 3D and 2D imaging data for assured navigation in unknown environments: initial steps", Proc. SPIE 7323, Laser Radar Technology and Applications XIV, 732307 (9 May 2009);

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