Submillimeter-Accurate Markerless Hand-Eye Calibration Based on a Robot's Flange Features.

An accurate and reliable estimation of the transformation matrix between an optical sensor and a robot is a key aspect of the hand-eye system calibration process in vision-guided robotic applications. This paper presents a novel approach to markerless hand-eye calibration that achieves streamlined, flexible, and highly accurate results, even without error compensation. The calibration procedure is mainly based on using the robot's tool center point (TCP) as the reference point. The TCP coordinate estimation is based on the robot's flange point cloud, considering its geometrical features. A mathematical model streamlining the conventional marker-based hand-eye calibration is derived. Furthermore, a novel algorithm for the automatic estimation of the flange's geometric features from its point cloud, based on a 3D circle fitting, the least square method, and a nearest neighbor (NN) approach, is proposed. The accuracy of the proposed algorithm is validated using a calibration setting ring as the ground truth. Furthermore, to establish the minimal required number and configuration of calibration points, the impact of the number and the selection of the unique robot's flange positions on the calibration accuracy is investigated and validated by real-world experiments. Our experimental findings strongly indicate that our hand-eye system, employing the proposed algorithm, enables the estimation of the transformation between the robot and the 3D scanner with submillimeter accuracy, even when using the minimum of four non-coplanar points for calibration. Our approach improves the calibration accuracy by approximately four times compared to the state of the art, while eliminating the need for error compensation. Moreover, our calibration approach reduces the required number of the robot's flange positions by approximately 40%, and even more if the calibration procedure utilizes just four properly selected flange positions. The presented findings introduce a more efficient hand-eye calibration procedure, offering a superior simplicity of implementation and increased precision in various robotic applications.

Application of unmanned aerial system structure from motion point cloud detected tree heights and stem diameters to model missing stem diameters.

Monitoring of tree spatial arrangement is increasingly essential for restoration of dry conifer forests. The presented method was developed for high-density point clouds, like those from unmanned aerial system imagery, to extract and model individual tree location, height, and diameter at breast height (DBH). Extraction of tree locations and heights uses a variable window function searching point cloud-derived canopy height models. Tree DBH is extracted for a subset of point cloud trees using a slice at 1.32-1.42 m and a least-squares circle fitting algorithm. Extracted heights and DBHs are spatially matched and filtered against each tree's expected DBH predicted using a regional National Forest Inventory height to DBH relationship. Values remaining after filtering are used to create a site-specific height to DBH relationship for predicting missing DBH values. Applying the method in a ponderosa pine-dominated forest found that extracted height values exceeded the precision of field height measurement approaches, while the accuracy of extracted and modeled DBH values had a mean error of 0.79 cm.•Leveraging National Forest Inventory to filter DBH values eliminates the need for in situ observations.•Produces tree list for all extractable stems in the point cloud.•Transferable to high-density point clouds in open-canopy forests.

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits.

Ring rot caused by Botryosphaeria dothidea and anthracnose caused by Colletotrichum gloeosporioides are two important apple fruit diseases. It is critical to conduct timely and accurate distinction and diagnosis of the two diseases for apple disease management and apple quality control. The automatic distinction between the two diseases was investigated based on image processing technology in this study. The acquired disease images were preprocessed via image scaling, color image contrast stretching, and morphological opening and closing reconstruction. Then, two lesion segmentation methods based on circle fitting were proposed and used to conduct lesion segmentation. After comparison with the manual segmentation results obtained via the software Adobe Photoshop CC, Lesion segmentation method 1 was chosen for further disease image processing. The gray images on the nine components in the RGB, HSI, and L*a*b* color spaces of the segmented lesion images were filtered by using multi-scale block local binary pattern operators with the sizes of pixel blocks of 1 × 1, 2 × 2, and 3 × 3, respectively, and the corresponding local binary pattern (LBP) histogram vectors were calculated as the features of the lesion images. Subsequently, support vector machine (SVM) models and random forest models were built based on individual LBP histogram features or different LBP histogram feature combinations for distinguishing the diseases. The optimal SVM model with the distinction accuracies of the training and testing sets equal to 100 and 95.12% and the optimal random forest model with the distinction accuracies of the training and testing sets equal to 100 and 90.24% were achieved. The results indicated that the distinction between the two diseases could be implemented with high accuracy by using the proposed method. In this study, a method based on image processing technology was provided for the distinction of ring rot and anthracnose on apple fruits.

Detrending Technique for Denoising in CW Radar.

A detrending technique is proposed for continuous-wave (CW) radar to remove the effects of direct current (DC) offset, including DC drift, which is a very slow noise that appears near DC. DC drift is mainly caused by unwanted vibrations (generated by the radar itself, target objects, or surroundings) or characteristic changes in components in the radar owing to internal heating. It reduces the accuracy of the circle fitting method required for I/Q imbalance calibration and DC offset removal. The proposed technique effectively removes DC drift from the time-domain waveform of the baseband signals obtained for a certain time using polynomial fitting. The accuracy improvement in the circle fitting by the proposed technique using a 5.8 GHz CW radar decreases the error in the displacement measurement and increases the signal-to-noise ratio (SNR) in vital signal detection. The measurement results using a 5.8 GHz radar show that the proposed technique using a fifth-order polynomial fitting decreased the displacement error from 1.34 mm to 0.62 mm on average when the target was at a distance of 1 m. For a subject at a distance of 0.8 m, the measured SNR improved by 7.2 dB for respiration and 6.6 dB for heartbeat.

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application.

Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point cloud for extracting total tree height and diameter at breast height (1.3 m; DBH). We compared two different methods to accurately estimate total tree heights: the first method was based on a modified version of the local maxima algorithm for treetop detection, "HTTD", and for the second method we used the centers of stem cross-sections at stump height (30 cm), "HTSP". DBH was estimated by a computationally robust algebraic circle-fitting algorithm through hierarchical cluster analysis (HCA). This study aimed to assess the accuracy of these descriptors for evaluating total stem volume by comparing the results with the reference tree measurements. The difference between the estimated total stem volume from HTTD and measured stems was 2.732 m3 for European oak and 2.971 m3 for Norway spruce; differences between the estimated volume from HTSP and measured stems was 1.228 m3 and 2.006 m3 for European oak and Norway spruce, respectively. The coefficient of determination indicated a strong relationship between the measured and estimated total stem volumes from both height estimation methods with an R2 = 0.89 for HTTD and R2 = 0.87 for HTSP for European oak, and R2 = 0.98 for both HTTD and HTSP for Norway spruce. Our study has demonstrated the feasibility of finer-resolution remote sensing data for semi-automatic stem volumetric modeling of small-scale studies with high accuracy as a potential advancement in precision forestry.

Calibration of In-Plane Center Alignment Errors in the Installation of a Circular Slide with Machine-Vision Sensor and a Reflective Marker.

This paper describes a method for calibrating in-plane center alignment error (IPCA) that occurs when installing the circular motion slide (CMS). In this study, by combini ng the moving carriage of the CMS and the planar PKM (parallel kinematic mechanism) with the machine tool, the small workspace of the PKM is expanded, and the workpiece is placed on the table with the CMS installed is processed through the machine tool. However, to rigidly mount the CMS on the table, the preload between the guide and the support bearings must be adjusted with the eccentric bearing, and in this process, the IPCA occurs. After installing a reflective marker on the PKM, the PKM is slowly rotated along with the ring guide in the way of stop-and-go without the PKM's own motion. Then, using a machine vision camera installed at the top of the CMS, the IPCA, which is the difference between the actual center position and the nominal center position of the CMS with respect to the camera, can be successfully calibrated through the circular fitting process. Consequently, it was confirmed that the IPCA of 0.37 mm can be successfully identified with the proposed method.

A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation.

Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images.