IT-HYFAO-VO: Interpretation Tree-Based VO With Hybrid Feature Association and Optimization

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For the visual odometry (VO) and simultaneous localization and mapping (SLAM) in indoor environments, high-level geometric features have attracted more and more attention in recent years. Unlike the generally used point features, the geometric features, such as planes and lines, encode more higher-level semantic information of the scene which is beneficial for various tasks of mobile robots. In this article, an RGB-D VO system with hybrid high-level geometric features is developed. An interpretation tree (IT)-based hybrid feature association framework is proposed, which turns the feature association into a multiple-hypothesis decision problem. The IT expansion method is elaborately designed. Specifically, an internode consistency is proposed for generation of hypotheses and a consistent transformation model (CTM) for each hypothesis is explicitly expressed and incrementally updated. When the IT is constructed, a closed-form solution to the feature association and the camera transformation can be obtained. Then, a hybrid feature joint optimization method is introduced to further refine the pose estimate and parameters of geometric features. During optimization, the planes and lines are appropriately parameterized and the uncertainties arising from feature extraction are derived and used to balance the contributions of two types of features in the cost function. Extensive experiments are executed on public datasets and the results demonstrate that the proposed method can achieve higher accuracy and stronger robustness.