IIIT Hyderabad Publications |
|||||||||
|
Integrating Objects into Monocular SLAM: Line Based Category Specific ModelsAuthor: Nayan Joshi 201331023 Date: 2021-07-29 Report no: IIIT/TH/2021/134 Advisor:Madhava Krishna AbstractThe thesis talks about our novel architecture about the quasi-static world to formulate real-time object-based monocular Simultaneous Localization and Mapping (SLAM) for objects present in a scene. Here we propose our novel line-based parameterization for category-specific 3D CAD models. Using the vastly available 3D CAD model collection we have created category-level models for objects, which is in contrast with existing approaches that incorporate object-level models. The proposed parameterization associates the 3D category-specific CAD model and the object present in our scene using a dictionary-based RANSAC method that employs object viewpoint as prior and edges detected in the respective intensity image of the scene. The association problem posed here is tackled as a classical geometry problem rather than being dataset driven, thus saving the time and labor that one invests in annotating dataset to train Keypoint Network[25, 26] for different category objects. The proposed approach not only eliminates the need for dataset preparation, collecting a huge amount of object-level models, and annotating them but also speeds up the entire process as this method processes the image only once for all objects, thus eliminating the need of invoking the network for every object in an image across all the frames. A 3D-2D edge association module followed by a resection algorithm for lines is used to recover object poses. The formulation optimizes for the shape and pose of the object, thus aiding in recovering object 3D structure more accurately. Finally, a Factor Graph formulation is used to combine object poses with camera odometry that helps in formulating the optimization as a SLAM problem. The proposed category-level model-based approach has several other applications, such as retrieval of objects, which is an integral part of Augmented Reality applications. The architecture has been evaluated both qualitatively and quantitatively in a variety of indoor real-world environments, infused with several camera and objects motion challenges, depicting the usefulness of an instance independent monocular object SLAM and the advantage it enjoys over existing feature-based SLAM approaches. Full thesis: pdf Centre for Robotics |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |