Full Pose Estimation Research Articles

A Transformer‐Based Network for Full Object Pose Estimation with Depth Refinement

In response to increasing demand for robotics manipulation, accurate vision‐based full pose estimation is essential. While convolutional neural networks‐based approaches have been introduced, the quest for higher performance continues, especially for precise robotics manipulation, including in the Agri‐robotics domain. This article proposes an improved transformer‐based pipeline for full pose estimation, incorporating a Depth Refinement Module. Operating solely on monocular images, the architecture features an innovative Lighter Depth Estimation Network using a Feature Pyramid with an up‐sampling method for depth prediction. A Transformer‐based Detection Network with additional prediction heads is employed to directly regress object centers and predict the full poses of the target objects. A novel Depth Refinement Module is then utilized alongside the predicted centers, full poses, and depth patches to refine the accuracy of the estimated poses. The performance of this pipeline is extensively compared with other state‐of‐the‐art methods, and the results are analyzed for fruit picking applications. The results demonstrate that the pipeline improves the accuracy of pose estimation to up to 90.79% compared to other methods available in the literature.

Heterogeneous Multisensor Fusion for Mobile Platform Three-Dimensional Pose Estimation

Precise, robust, and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and simultaneous localization and mapping (SLAM). To estimate the pose of a platform, sensors such as inertial measurement units (IMUs), global positioning system (GPS), and cameras are commonly employed. Each of these sensors has their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this paper, a three-dimensional (3D) pose estimation algorithm is presented for a unmanned aerial vehicle (UAV) in an unknown GPS-denied environment. A UAV can be fully localized by three position coordinates and three orientation angles. The proposed algorithm fuses the data from an IMU, a camera, and a two-dimensional (2D) light detection and ranging (LiDAR) using extended Kalman filter (EKF) to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a two-dimensional (2D) LiDAR can only provide pose estimation in its scanning plane, and thus, it cannot obtain a full pose estimation in a 3D environment. A novel method is introduced in this paper that employs a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera, and it is shown that this method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments.

Design and Optimization Strategy of Sensor Array Layout for Magnetic Localization System

Magnetic tracking technology is emerging to provide an occlusion-free tracking scheme for the estimation of full pose of various instruments. This brings substantial benefits for intra-corporeal applications, such as tracking of flexible robots or wireless endoscopic devices, and thus is helpful for further computer-assisted diagnosis, interventions, and surgeries. Towards efficient magnetic tracking, we propose a magnetic localization and orientation system in this paper. By modeling the cylindrical magnet, the magnetic models have been compared and analyzed. Moreover, we present a sensor layout strategy based on grid method and its optimization method for better performance. Based on the magnetic model and the layout optimization results, we have built a sensor array for magnetic positioning. Extensive simulations and experiments have shown the feasibility of the proposed system with the average positional and orientational errors of $1.4 mm$ and 3.4°, respectively.

Multi-sensor 3D object dataset for object recognition with full pose estimation

In this work, we propose a new dataset for 3D object recognition using the new high-resolution Kinect V2 sensor and some other popular low-cost devices like PrimeSense Carmine. Since most already existing datasets for 3D object recognition lack some features such as 3D pose information about objects in the scene, per pixel segmentation or level of occlusion, we propose a new one combining all this information in a single dataset that can be used to validate existing and new 3D object recognition algorithms. Moreover, with the advent of the new Kinect V2 sensor we are able to provide high-resolution data for RGB and depth information using a single sensor, whereas other datasets had to combine multiple sensors. In addition, we will also provide semiautomatic segmentation and semantic labels about the different parts of the objects so that the dataset could be used for testing robot grasping and scene labeling systems as well as for object recognition.

6-D Magnetic Localization and Orientation Method for an Annular Magnet Based on a Closed-Form Analytical Model

Magnetic tracking technology is emerging to provide an occlusion-free tracking scheme for the estimation of full pose (position and orientation) of various instruments. This brings substantial benefits for intracorporeal applications, such as for tracking of flexible or wireless endoscopic devices, and thus is significant for further computer-assisted diagnosis, interventions, and surgeries. Toward efficient magnetic tracking, a 6-D magnetic localization and orientation method is proposed in this paper. An annular permanent magnet is mounted on the exterior surface of a capsule. With a magnetic sensor array, the magnetic field can be measured and the capsule's 3-D location and 3-D orientation information can be estimated based on proposed closed-form analytical model of annular magnet and particle swarm optimization algorithm. Magnetic dipole model and Levenberg-Marquardt algorithm are used to improve the speed and accuracy of estimation. Extensive simulation experiments show that the localization and orientation method works well with good position and orientation accuracy.