Date

2014

Author

Gedik, Osman Serdar

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3D tracking of objects is essential in many applications, such as robotics and aug- mented reality (AR), and availability of accurate pose estimates increases reliability in robotic applications whereas decreases jitter in AR scenarios. As a result of the recent advances in the sensor technology, it is possible to capture synchronous high frame rate RGB and depth data. With this motivation, an automated and high accurate 3D tracking algorithm based on simultaneous utilization of visual and depth sensors is presented. The depth sensor data is utilized both in raw format and in the form of Shape Index Map (SIM), after the observation that the latter transformation empha- sizes structural details and provides a proper basis to jointly utilize both sensor data. As the object model, the initial object colored point cloud is utilized, which elimi- nates dependency on any offline generated Computer Aided Design (CAD) models that might limit the application areas. A typical 3D tracking algorithm composes of the following stages: Feature selection, feature association between consecutive frames and 3D pose estimation by the feature correspondences. Since the main aim is to perform highly accurate 3D tracking of any user selected object, data from both sensors is exploited in every stage of the process for improving accuracy, as well as robustness. First of all, a novel feature selection method, which localizes features with high textural and spatial cornerness properties, is proposed. In this method, in order to increase the spatial spread of features around the object, the region of interest is divided into regular grids. Within each grid a single feature with maximum cornerness measure in terms of both intensity and SIM data is selected. Imposing spatial-textural constraints jointly selects more discrimina- tive features, whereas a regular grid-based approach decreases bias on pose estimates. Then, the selected features are associated between consecutive frames by a new fea- ture tracking approach, which tracks each feature independently and simultaneously on intensity and SIM data for improving 3D tracking performance. The method de- cides on the final feature association based on the reliabilities of individual trackers estimated online. Such a parallel approach is observed to increase robustness against sensor noise and individual tracker failures. Finally, RGB and depth measurements of localized features are fused in a well-known Extended Kalman Filter (EKF) frame- work. In this framework, we propose a novel measurement weighting scheme, based on the manipulation of Kalman gain term, which favors high quality features and pro- vides robustness against measurement errors. This scheme, establishing a connection between computer vision and Bayes filtering disciplines, eliminates sole dependency on predefined sensor noise parameters and identical measurement noise assumption. The increase in 3D tracing accuracy due to each proposed sub-system is shown via experimental results. Furthermore, the accuracy of the proposed 3D tracking method is tested against a number of well-known techniques from the literature and supe- rior performance is observed against such approaches. Finally, the resulting pose estimates of the proposed algorithm is utilized to obtain 3D maps after combining colored point clouds at consecutive time instants. We observe that, although loop clo- sure or post-processing algorithms are not exploited, significant number of 3D point clouds are combined with a quite high accuracy.

Subject Keywords

Three-dimensional imaging., Multisensor data fusion., Tracking (Engineering)., Robotics.

URI

http://etd.lib.metu.edu.tr/upload/12618071/index.pdf
https://hdl.handle.net/11511/23955

Collections

Graduate School of Natural and Applied Sciences, Thesis