Robust Visual-Inertial Sensor Fusion For Navigation, Localization, Mapping, And 3D Reconstruction
- 技术优势
- Uses integrated inertial and vision measurementsImproved robustness and performanceFocuses on handling outliers
- 技术应用
- Augmented and virtual realityRoboticsAutonomous vehicles and flying robotsIndoor localization in GPS-denied areasEgo-motion estimation
- 详细技术说明
- Researchers led by Professor Stefano Soatto have developed a novel sensor fusion system that integrates inertial and vision measurements to estimate 3D positon and orientation, along with a point-cloud model of the 3D world surrounding it. This invention has better robustness and performance that other performing VINS schemes, such as Google Tango, and with the same computational footprint. This unique technology addresses the problem of inferring ego-motion of a sensor platform from visual and inertial measurements, focusing on handling outliers.
- *Abstract
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UCLA researchers in the Computer Science Department have invented a novel model for a visual-inertial system (VINS) for navigation, localization, mapping, and 3D reconstruction applications.
- *IP Issue Date
- May 19, 2016
- *Principal Investigation
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Name: Stefano Soatto
Department:
Name: Konstantine Tsotsos
Department:
- 申请号码
- 20160140729
- 其他
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Background
Vision-augmented navigation or VINS is central to augmented and virtual reality, robotics, autonomous vehicles, and navigation in mobile phones. The future growth of these applications depends on reliable navigation in dynamic environments, thus improvement to these systems is of importance. Current methods rely upon low-level processing of visual data for 3D motion estimation. However, the processing is substantially useless and easily 60 – 90% of sparse features selected and tracked across frames are inconsistent with a single rigid motion due to illumination effects, occlusions, and independently moving objects. These effects are global to the scene, while low-level processing is local to the image, so it is not realistic to expect significant improvements in the vision front-end. Instead, it is critical for algorithms utilizing vision to leverage other sensory modalities, such as inertial.
Related Materials
E. S. Jones and S. Soatto. Visual-Inertial Navigation, Mapping and Localization: A Scalable Real-Time Causal Approach. The International Journal of Robotics Research. 2011.
K. Tsotsos, A. Chiuso, and S. Soatto. Robust Inference for Visual-Inertial Sensor Fusion. 2015 IEEE International Conference on Robotics and Automation. 2015.Additional Technologies by these Inventors
- Method and Apparatus for Detecting, Tracking, Recognizing, and Categorizing Objects and Scenes from Video
- Dsp-Sift: Domain-Size Pooling For Image Descriptors For Image Matching And Other Applications
Tech ID/UC Case
27401/2015-346-0
Related Cases
2015-346-0
- 国家/地区
- 美国
