Symmetric Object Pose Estimation via Flexible Modular CNN

Object pose estimation is a crucial task in various applications, including human-robot interaction, mobile robotics, and augmented reality. It involves determining the position and orientation of an object relative to a reference frame. This is a challenging task due to the need for accurate object detection and recognition, as well as understanding its geometry and the surrounding environment. Depending on the application and available resources, this task can be performed using Lidars, as in autonomous driving, or smaller RGBD cameras, as in mobile robotics. This work proposes an innovative convolutional neural network (CNN) for object pose estimation from RGBD data. The model is designed to have two separate branches, one for estimating the object's position and one for estimating the orientation, to facilitate the training process without loss in performance. Moreover, our approach emphasizes the problem of symmetric object pose estimation, for which we designed a new loss function to better represent the rotation error. The proposed model, with the newly introduced loss function, outperforms state of the art models on public datasets for object pose estimate, both for standard asymmetric objects and symmetric ones.