Local Pose Estimation Research Articles

MULTI-ROBOT COOPERATIVE LIDAR SLAM FOR EFFICIENT MAPPING IN URBAN SCENES

Abstract. We first use the multi-robot SLAM framework DiSCo-SLAM to evaluate the performance of cooperative SLAM based on the complicated dataset in urban scenes. Besides, we perform comparisons of single-robot SLAM and multi-robot SLAM to explore whether the cooperative framework can noticeably improve robot localization performance and the influence of inter-robot constraints in local pose graph, utilizing an identical dataset generated via the Carla simulator. Our findings indicate that under specific conditions, the integration of inter-robot constraints may effectively mitigate drift in local pose estimation. The extent to which inter-robot constraints affect the correction of local SLAM is related to various factors, such as the confidence level of the constraints and the range of keyframes imposed by the constraint.

Markerless gait estimation and tracking for postural assessment

Postural assessment is crucial in the sports screening system to reduce the risk of severe injury. The capture of the athlete’s posture using computer vision attracts huge attention in the sports community due to its markerless motion capture and less interference in the physical training. In this paper, a novel markerless gait estimation and tracking algorithm is proposed to locate human key-points in spatial-temporal sequences for gait analysis. First, human pose estimation using OpenPose network to detect 14 core key-points from the human body. The ratio of body joints is normalized with neck-to-pelvis distance to obtain camera invariant key-points. These key-points are subsequently used to generate a spatial-temporal sequences and it is fed into Long-Short-Term-Memory network for gait recognition. An indexed person is tracked for quick local pose estimation and postural analysis. This proposed algorithm can automate the capture of human joints for postural assessment to analyze the human motion. The proposed system is implemented on Intel Up Squared Board and it can achieve up to 9 frames-per-second with 95% accuracy of gait recognition.

Fast 6D object pose estimation of shell parts for robotic assembly

Shell parts which have symmetric structure, even surface and flat color are common in industrial manufacturing applications. Moreover, the inner and outer surface of them are of similar shape and close to each other. These features make the pose estimation process of the shell parts challenging. Aiming at 6D pose error compensation of parts in high-precision robotic assembly tasks, this work proposes a fast 6D pose estimation method tailored for the shell parts. With a binocular structured light camera to acquire the point cloud data, the proposed method consists of two phases, namely the initial pose estimation phase and local pose estimation phase. In the former phase, principle component analysis algorithm is utilized to calculate a primary pose estimation. An initial pose correction and translation offset methods serve to solve the local optimal estimation problem of the iterative closest point (ICP) algorithm. In the latter phase, the voxel sampling and a novel weighted point-to-plane ICP algorithms are applied to boost the efficiency of the pose estimation method. With two typical shell parts, a simulation and an experiment of pose estimation are conducted to validate the effectiveness of the proposed method. Experiment results prove that both the accuracy and efficiency of the pose estimation method meet the requirement of the given assembly tasks.

Wide baseline pose estimation from video with a density-based uncertainty model

Robust wide baseline pose estimation is an essential step in the deployment of smart camera networks. In this work, we highlight some current limitations of conventional strategies for relative pose estimation in difficult urban scenes. Then, we propose a solution which relies on an adaptive search of corresponding interest points in synchronized video streams which allows us to converge robustly toward a high-quality solution. The core idea of our algorithm is to build across the image space a nonstationary mapping of the local pose estimation uncertainty, based on the spatial distribution of interest points. Subsequently, the mapping guides the selection of new observations from the video stream in order to prioritize the coverage of areas of high uncertainty. With an additional step in the initial stage, the proposed algorithm may also be used for refining an existing pose estimation based on the video data; this mode allows for performing a data-driven self-calibration task for stereo rigs for which accuracy is critical, such as onboard medical or vehicular systems. We validate our method on three different datasets which cover typical scenarios in pose estimation. The results show a fast and robust convergence of the solution, with a significant improvement, compared to single image-based alternatives, of the RMSE of ground-truth matches, and of the maximum absolute error.

Ultra-Wideband Based Pose Estimation for Small Unmanned Aerial Vehicles

This paper proposes a 3-D local pose estimation system for a small Unmanned Aerial Vehicle (UAV) with a weight limit of 200 g and a very small footprint of 10 cm $\times$ 10 cm. The system is realized by fusing 3-D position estimations from an Ultra-Wide Band (UWB) transceiver network with Inertial Measurement Unit (IMU) sensor data and data from a barometric pressure sensor. The 3-D position from the UWB network is estimated using Multi-Dimensional Scaling (MDS) and range measurements between the transceivers. The range measurements are obtained using Double-Sided Two-Way Ranging (DS-TWR), thus eliminating the need for an additional clock synchronization mechanism. The sensor fusion is accomplished using a loosely coupled Extended Kalman Filter (EKF) architecture. Extensive evaluation of the proposed system shows that a position accuracy with a Root-Mean-Square Error (RMSE) of $0.20\,cm$ can be obtained. The orientation angle can be estimated with an RMSE of 1.93°.

Micro air vehicle local pose estimation with a two-dimensional laser scanner: A case study for electric tower inspection

Automation of inspection tasks is crucial for the development of the power industry, where micro air vehicles have shown a great potential. Self-localization in this context remains a key issue and is the main subject of this work. This article presents a methodology to obtain complete three-dimensional local pose estimates in electric tower inspection tasks with micro air vehicles, using an on-board sensor set-up consisting of a two-dimensional light detection and ranging, a barometer sensor and an inertial measurement unit. First, we present a method to track the tower’s cross-sections in the laser scans and give insights on how this can be used to model electric towers. Then, we show how the popular iterative closest point algorithm, that is typically limited to indoor navigation, can be adapted to this scenario and propose two different implementations to retrieve pose information. This is complemented with attitude estimates from the inertial measurement unit measurements, based on a gain-scheduled non-linear observer formulation. An altitude observer to compensate for barometer drift is also presented. Finally, we address velocity estimation with views to feedback position control. Validations based on simulations and experimental data are presented.

Metric-based detection of robot kidnapping with an SVM classifier

Kidnapping occurs when a robot is unaware that it has not correctly ascertained its position, potentially causing severe map deformation and reducing the robot’s functionality. This paper presents metric-based techniques for real-time kidnap detection, utilising either linear or SVM classifiers to identify all kidnapping events during the autonomous operation of a mobile robot. In contrast, existing techniques either solve specific cases of kidnapping, such as elevator motion, without addressing the general case or remove dependence on local pose estimation entirely, an inefficient and computationally expensive approach. Three metrics that measured the quality of a pose estimate were evaluated and a joint classifier was constructed by combining the most discriminative quality metric with a fourth metric that measured the discrepancy between two independent pose estimates. A multi-class Support Vector Machine classifier was also trained using all four metrics and produced better classification results than the simpler joint classifier, at the cost of requiring a larger training dataset. While metrics specific to 3D point clouds were used, the approach can be generalised to other forms of data, including visual, provided that two independent ways of estimating pose are available.

Fringe inverse videogrammetry based on global pose estimation

Fringe inverse videogrammetry based on global pose estimation is presented to measure a three-dimensional (3D) coordinate. The main components involve an LCD screen, a tactile probe equipped with a microcamera, and a portable personal computer. The LCD is utilized to display fringes, a microcamera is installed on the tactile probe, and the 3D coordinate of the center of the probe tip can be calculated through the microcamera's pose. Fourier fringe analysis is exploited to complete subpixel location of reference points. A convex-relaxation optimization algorithm is employed to estimate the global camera pose, which guarantees global convergence compared with bundle adjustment, a local pose estimation algorithm. The experiments demonstrate that fringe inverse videogrammetry can measure the 3D coordinate precisely.

The Elastic View Graph Framework for Autonomous, Surface-based 3D-SLAM Part II: Global Layer and Experiments (Schritthaltendes flächenbasiertes 3D-SLAM mit elastisch gekoppelten Teilkarten)

This paper covers the global aspects of a new SLAM framework introduced in [9]. The map is a graph of overlapping range views, linked by multiple uncertain hypotheses of motion and correspondence. They form the interface between local pose estimation and globally consistent mapping. In order to prune the hypotheses and reduce the brittleness of the local algorithms, we propose a novel cooperation between image-based and odometric motion estimates, and a geometric-probabilistic visibility model for oriented surface features which can also discern moving objects. Global loop closing works by exchanging hypotheses, priorized on a node ambiguity measure to bound the update complexity. The cycle error in frame space is used as a consistency criterion. The new concepts were tested and evaluated during several indoor exploration tours.