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公开(公告)号:US11525683B2
公开(公告)日:2022-12-13
申请号:US16254303
申请日:2019-01-22
Applicant: Tata Consultancy Services Limited
Inventor: Vishvendra Rustagi , Mohit Ludhiyani , Arnab Sinha , Ranjan Dasgupta
Abstract: Embodiments of the present disclosure provide systems and methods to eliminate (or filter) drift for dynamics model based localization of multirotors. The dynamics equations require drag modelling, which is dependent on velocity, to generate vehicles' acceleration along the body axis. The present disclosure considers the drag contribution, at velocity level, as a low frequency component. Incorrect or nonmodelling of this low frequency component leads to drift at velocity level. This drift can then be removed through a high pass filter to obtain drift free velocity data for pose estimation and better localization thereof.
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2.
公开(公告)号:US10748299B2
公开(公告)日:2020-08-18
申请号:US16580403
申请日:2019-09-24
Applicant: Tata Consultancy Services Limited
Inventor: Mohit Ludhiyani , Vishvendra Rustagi , Arnab Sinha , Ranjan Dasgupta
Abstract: Robotic vision-based framework wherein an on-board camera device is used for scale estimation. Unlike conventional scale estimation methods that require inputs from more than one or more sensors, implementations include a system and method to estimate scale online solely, without any other sensor, for monocular SLAM by using multirotor dynamics model in an extended Kalman filter framework. This approach improves over convention scale estimation methods which require information from some other sensors or knowledge of physical dimension of an object within the camera view. An arbitrary scaled position and an Euler angle of a multirotor are estimated from vision SLAM (simultaneous localization and mapping) technique. Further, dynamically integrating, computed acceleration to estimate a metric position. A scale factor and a parameter associated with the multirotor dynamics model is obtained by comparing the estimated metric position with the estimated arbitrary position.
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