Depth estimation with ego-motion assisted monocular camera
| Autor: | Pavel Davidson, M. Mansour, Jukka-Pekka Raunio, Oleg A. Stepanov, Robert Piche, Mohammad M. Aref |
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| Přispěvatelé: | Tampere University, Computing Sciences, Automation Technology and Mechanical Engineering, Research group: Innovative Hydraulic Automation, Research group: Automation and Systems Theory |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
General Computer Science Observer (quantum physics) Computer science business.industry 010401 analytical chemistry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Kinematics Sensor fusion 113 Computer and information sciences 01 natural sciences Odometer 0104 chemical sciences Computer Science::Robotics Extended Kalman filter Control and Systems Engineering Feature (computer vision) Inertial measurement unit Point (geometry) Computer vision Artificial intelligence Electrical and Electronic Engineering business 0105 earth and related environmental sciences |
| Popis: | —: We propose a method to estimate the distance to objects based on the complementary nature of monocular image sequences and camera kinematic parameters. The fusion of camera measurements with the kinematics parameters that are measured by an IMU and an odometer is performed using an extended Kalman filter. Results of field experiments with a wheeled robot corroborated the results of the simulation study in terms of accuracy of depth estimation. The performance of the approach in depth estimation is strongly affected by the mutual observer and feature point geometry, measurement accuracy of the observer’s motion parameters and distance covered by the observer. It was found that under favorable conditions the error in distance estimation can be as small as 1% of the distance to a feature point. This approach can be used to estimate distance to objects located hundreds of meters away from the camera. acceptedVersion |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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