Thermal Physiological Moment Invariants for Face Identification

3D object pose estimation by using a depth sensor is one of the important tasks in activities by robots. To reduce the pose ambiguity of an estimated object pose, several methods for multiple viewpoint pose estimation have been proposed. However, these methods need to select the viewpoints carefully to obtain better results. If the pose of the target object is ambiguous from the current observation, we could not decide where we should move the sensor to set as the next viewpoint. In this paper, we propose a best next viewpoint recommendation method by minimizing the pose ambiguity of the object by making use of the current pose estimation result as a latent variable. We evaluated viewpoints recommended by the proposed method and confirmed that it helps us to gain better pose estimation results than several comparative methods on a synthetic dataset.

Vehicle pose estimation with LIDAR plays a crucial role in autonomous driving systems. It serves as the fundamental basis for functions such as tracking, path planning, and decision-making. However, the majority of current vehicle pose estimation techniques struggle to produce satisfactory results when faced with incomplete observation measurements, such as L-shaped point cloud clusters without side contours or those including side-view mirrors. In addition, the requirement for real-time results further increases the difficulty of the pose estimation task. In this paper, we present a vehicle Pose Estimation method with Heuristic L-shape fitting and grid-based Particle Filter (PE-HL-PF). We design a geometric shape classifier module to divide clusters into symmetrical and asymmetrical ones according to their shape features. Furthermore, a contour-based heuristic L-shape fitting module is introduced for asymmetrical clusters, and a structure-aware grid-based particle filter is used to estimate the pose of symmetrical clusters. PE-HL-PF first utilizes a heuristic asymmetrical module that selects dominant contours fitting orientation in a heuristic manner, thereby avoiding the need for a complex traversal search. Additionally, a symmetrical module based on particle filtering is incorporated to enhance the stability of orientation estimation. This method achieves significant improvements in both the runtime efficiency and pose estimation accuracy of incomplete point clouds. Compared with state-of-the-art pose estimation methods, our PE-HL-PF demonstrates a notable performance improvement. Our method can estimate the pose of thousands of objects in less than 1 millisecond, a significant improvement over previous methods. The results of experiments performed on the KITTI dataset validate the effectiveness of our approach.

We present the first method to perform automatic 3D pose, shape and texture capture of animals from images acquired in-the-wild. In particular, we focus on the problem of capturing 3D information about Grevy's zebras from a collection of images. The Grevy's zebra is one of the most endangered species in Africa, with only a few thousand individuals left. Capturing the shape and pose of these animals can provide biologists and conservationists with information about animal health and behavior. In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other. To overcome these challenges, we integrate the recent SMAL animal model into a network-based regression pipeline, which we train end-to-end on synthetically generated images with pose, shape, and background variation. Going beyond state-of-the-art methods for human shape and pose estimation, our method learns a shape space for zebras during training. Learning such a shape space from images using only a photometric loss is novel, and the approach can be used to learn shape in other settings with limited 3D supervision. Moreover, we couple 3D pose and shape prediction with the task of texture synthesis, obtaining a full texture map of the animal from a single image. We show that the predicted texture map allows a novel per-instance unsupervised optimization over the network features. This method, SMALST (SMAL with learned Shape and Texture) goes beyond previous work, which assumed manual keypoints and/or segmentation, to regress directly from pixels to 3D animal shape, pose and texture. Code and data are available at https://github.com/silviazuffi/smalst.

The Global Positioning System (GPS) is interfered easily, for the need of Unmanned Aerial Vehicle (UAV)'s landing automatically and accurately in all weather, a new program of UAV's automatic landing is proposed based on the cooperative infrared object on the runway and the infrared computer vision in UAV. The cooperative object is recognized by affine moment invariants that is robust to geometric deformation from different big bevel angle of view in distant place. As there are some distortions of camera lens, then we used the characteristic points from the image of the cooperative infrared object to compute UAV's the angle of pitching, the angle of roll and the angle of yaw relative to the runway by Tsai calibration method that can rectify camera lens distortions. The experimental result shows that the average correct identification rate reaches 97%, better than the two prevenient algorithms based on the direction code and the traditional moment invariants, the time consumption of this new recognition algorithm is only 25% of the two algorithms. Simulations show that the method of pose estimation in this paper is full-informationed, accurate and feasible for the pose estimation of the UAV comparing with the results of references.

Pose detection of objects is an important topic in object-level mapping and indoor localization. In the past, pose estimation methods were performed either with the help of artificial markers or natural features found on the object. However, due to the fact that the markers can only be utilized in controlled environment experiments, the application of marker-based approaches is very limited. Furthermore, methods that depend on the object's natural visual features require texture on the object and lack robustness to illumination and camera viewpoint variations. With the advent of Deep Learning (DL), the classical pose estimation methods have been outperformed. The DL-based pose estimation can detect deep features of the object and exhibits higher robustness to many distortions and variabilities caused by the changes in the illumination and viewpoint conditions. However, the massive training data set requirement is the main challenge with most DL-based methods. The training set is often a real set of images that have been manually labeled or annotated. In addition, such methods face problems related to the degradation of their predicted accuracy in the presence of uncertainties due to the symmetrical structure of many objects. To address the aforementioned issues, a novel and very fast method for generating synthetic data, as well as a contour-based technique for accurate pose estimation (that can handle pose ambiguities for a symmetrical object) are proposed in this paper. The tests that are conducted in multiple indoor scenarios demonstrate not only the effectiveness of the synthetic data generation but also exhibit, in many cases, the very high accuracy of the proposed pose estimation method.

Vehicle pose estimation with light detection and ranging (LiDAR) is essential in the perception technology of autonomous driving. However, because of incomplete observation measurements and sparsity of the LiDAR point cloud, it is challenging to achieve satisfactory pose extraction based on 3D LiDAR with the existing pose estimation methods. In addition, the demand for real-time performance further increases the difficulty of the pose estimation task. In this paper, we propose a novel vehicle pose estimation method based on the convex hull. The extracted 3D cluster is reduced to the convex hull, reducing the subsequent computation burden while preserving essential contour information. Subsequently, a novel criterion based on the minimum occlusion area is developed for the search-based algorithm, enabling accurate pose estimation. Additionally, this criterion renders the proposed algorithm particularly well-suited for obstacle avoidance. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired at an industrial park. The results demonstrate that our proposed method can achieve better accuracy than the classical pose estimation method while maintaining real-time speed.

Deep learning method for 6D object pose estimation based on RGB image and depth (RGB-D) has been successfully applied to robot grasping. The fusion of RGB and depth is one of the most important difficulties. Previous works on the fusion of these two features are mostly concatenated together without considering the different contributions of the two types of features to pose estimation. We propose a selective embedding with gated fusion structure called SEGate, which can adjust the weights of RGB and depth features adaptively. Furthermore, we aggregate the local features of point clouds according to the distance between them. More specifically, the close point clouds contribute a lot to local features, while the distant point clouds contribute a little. Experiments show that our approach achieves the state-of-art performance in both LineMOD and YCB-Video datasets. Meanwhile, our approach is more robust to the pose estimation of occluded objects.

Light detection and ranging (LiDAR) sensors provide accurate 3-D point clouds for noncooperative spacecraft pose estimation. Several robust methods, such as iterative closest point, exist to perform a local refinement of the pose starting from an initial estimate. However, finding the initial pose of the spacecraft is a global optimization problem, which is challenging to solve in real time. This is especially true on space hardware with limited computing power. In addition, many spacecrafts have a shape with multiple symmetries, making an unambiguous initial pose estimation impossible. This work introduces a convolutional-neural-network-based pose estimation method, accounting for potential symmetries of the target satellite. The point clouds are projected to a 2-D depth image before being processed by the network. To generate a sufficient amount of training data, a LiDAR simulator integrating multiple effects such as reflections or laser beam divergence is developed. While being trained solely on synthetic point clouds, the pose estimation method shows to be precise, efficient, and reliable when evaluated on real point clouds taken at a hardware-in-the-loop rendezvous test facility. A runtime evaluation on potential space computing hardware is also performed to demonstrate the applicability of the method to real-time onboard pose estimation.

Comunicació presentada a la 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), celebrada els dies 30 de maig a 3 de juny de 2017 a Washington DC, EUA.

In the context of future manufacturing lines, removing fixtures will be a fundamental step to increase the flexibility of autonomous systems in assembly and logistic operations. Vision-based 3D pose estimation is a necessity to accurately handle objects that might not be placed at fixed positions during the robot task execution. Industrial tasks bring multiple challenges for the robust pose estimation of objects such as difficult object properties, tight cycle times and constraints on camera views. In particular, when interacting with objects, we have to work with close-range partial views of objects that pose a new challenge for typical view-based pose estimation methods. In this paper, we present a 3D pose estimation method based on a gradient-ascend particle filter that integrates new observations on-the-fly to improve the pose estimate. Thereby, we can apply this method online during task execution to save valuable cycle time. In contrast to other view-based pose estimation methods, we model potential views in full 6- dimensional space that allows us to cope with close-range partial objects views. We demonstrate the approach on a real assembly task, in which the algorithm usually converges to the correct pose within 10-15 iterations with an average accuracy of less than 8mm.

Estimating the external calibration --- the pose --- of a camera with respect to its environment is a fundamental task in Computer Vision (CV). In this paper, we propose a novel method for estimating the unknown 6DOF pose of a camera with known intrinsic parameters from epipolar geometry only. For a set of geo-located reference images, we assume the camera position - but not the orientation - to be known. We estimate epipolar geometry between the image of the query camera and the individual reference images using image features. Epipolar geometry inherently contains information about the relative positioning of the query camera with respect to each of the reference cameras, giving rise to a set of relative pose estimates. Combining the set of pose estimates and the positions of the reference cameras in a robust manner allows us to estimate a full 6DOF pose for the query camera. We evaluate our algorithm on different datasets of real imagery in indoor and outdoor environments. Since our pose estimation method does not rely on an explicit reconstruction of the scene, our approach exposes several significant advantages over existing algorithms from the area of pose estimation.

Six dimensional pose prediction of objects for robotic manipulation has received much attention in industrial applications. Despite considerable research on improving the performance of pose estimation, the problem remains challenging for industrial objects because of their textureless and mostly homogeneous properties in the presence of clutter and occlusion. This article proposes deep learning-based pose estimation by jointly detecting, segmenting and pose predicting. The binary classification branch as an attention module is suggested to improve the accuracy of instance segmentation. Using the instance information, the initial pose estimation network is designed by fusing the depth information into the grayscale image to strengthen the geometric features. Then, to obtain a highly accurate pose, an iterative network is constructed with point clouds as inputs to refine the initial pose. The networks are implemented to predict the pose of textureless object on the synthetic and real scenes. Experimental results indicate the pose estimation method is efficient and robust to pose prediction of textureless objects in cluttered and occluded scenes.

Bin-picking is a practical and challenging robotic manipulation task, where accurate 6D pose estimation plays a pivotal role. The workpieces in bin-picking are typically texture-less and randomly stacked in a bin, which poses a significant challenge to 6D pose estimation. Existing solutions are typically learning-based methods, which require object-specific training. Their efficiency of practical deployment for novel workpieces is highly limited by data collection and model retraining. Zero-shot 6D pose estimation is a potential approach to address the issue of deployment efficiency. Nevertheless, existing zero-shot 6D pose estimation methods are designed to leverage feature matching to establish point-to-point correspondences for pose estimation, which is less effective for workpieces with textureless appearances and ambiguous local regions. In this paper, we propose ZeroBP, a zero-shot pose estimation frame-work designed specifically for the bin-picking task. ZeroBP learns Position-Aware Correspondence (PAC) between the scene instance and its CAD model, leveraging both local features and global positions to resolve the mismatch issue caused by ambiguous regions with similar shapes and appearances. Extensive experiments on the ROBI dataset demonstrate that ZeroBP outperforms state-of-the-art zero-shot pose estimation methods, achieving an improvement of 9.1 % in average recall of correct poses.

Real-world traffic is highly dynamic, with pedestrians exhibiting unpredictable movements. Pedestrians' poses are essential cues for predicting their actions, enabling vehicles to respond proactively and reduce accident risks. In autonomous driving, the distance between vehicles and pedestrians is critical, making 3D human pose estimation crucial. In this context, pedestrian pose estimation has been actively studied, and recently, light detection and ranging (LiDAR) sensors have attracted attention due to their accurate 3D depth information and privacy benefits. However, existing LiDAR-based 3D pose estimation methods mainly process 3D data directly, requiring high computational cost and memory. In this paper, we propose a lightweight LiDAR-based 3D human pose estimation method specifically designed for deployment in autonomous driving systems. Unlike conventional 3D direct processing methods, our approach strategically reduces computational complexity by projecting point clouds into 2D depth images and leveraging a lightweight MoveNet, followed by efficient 3D lifting. Furthermore, we introduce a self-occlusion correction algorithm to improve robustness under side-view and bending poses, where depth-based projections often suffer from distortion. Experimental results on benchmark datasets demonstrate that the proposed method achieves competitive pose estimation accuracy while substantially improving efficiency, highlighting its practicality and scalability for real-time autonomous vehicle applications.

Aiming at the difficulty and low efficiency of pose estimation in the process of automatic drug picking, a pose estimation method combining plane segmentation and key point matching is proposed. First, a point cloud hierarchical clustering algorithm is used to segment the plane area, and the improved secondary key point matching method is used to identify and locate the pill box in each thread, which is convenient for multi-thread processing. Then the point cloud plane normal vector estimation method based on least squares is used to realize the posture reconstruction of the pill box. 600 images with viewpoint change, rotation transformation and scale change were collected for testing, the experimental results show that the pose estimation speed of the medicine box can reach 11 frames/s, the positioning deviation is less than 5 mm, and the pose estimation deviation is within 2°. So the method is efficient and can accurately identify the pose of the medicine box.