6D Pose Estimation using an Improved Method based on Point Pair Features

The Point Pair Feature (Drost et al. 2010) has been one of the most successful 6D pose estimation method among model-based approaches as an efficient, integrated and compromise alternative to the traditional local and global pipelines. During the last years, several variations of the algorithm have been proposed. Among these extensions, the solution introduced by Hinterstoisser et al. (2016) is a major contribution. This work presents a variation of this PPF method applied to the SIXD Challenge datasets presented at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV 2017. We report an average recall of 0.77 for all datasets and overall recall of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight, rutgers, tejani and doumanoglou datasets, respectively

Brain computerized tomography reading in suspected acute ischemic stroke patients: what are essentials for medical students?

Background Few systematic methods prioritize the image education in medical students (MS). We hope to develop a checklist of brain computerized tomography (CT) reading in patients with suspected acute ischemic stroke (AIS) for MS and primary care (PC) physicians. Methods Our pilot group generated the items indicating specific structures or signs for the checklist of brain CT reading in suspected AIS patients for MS and PC physicians. These items were used in a modified web-based Delphi process using the online software “SurveyMonkey”. In total 15 panelists including neurologists, neurosurgeons, neuroradiologists, and emergency department physicians participated in the modified Delphi process. Each panelist was encouraged to express feedback, agreement or disagreement on the inclusion of each item using a 9-point Likert scale. Items with median scores of 7–9 were included in our final checklist. Results Fifty-two items were initially provided for the first round of the Delphi process. Of these, 35 achieved general agreement of being an essential item for the MS and PC physicians. The other 17 of the 52 items in this round and another two added items suggested by the panelists were further rated in the next round. Finally, 38 items were included in the essential checklist items of brain CT reading in suspected AIS patients for MS and PC physicians. Conclusions We established a reference regarding the essential items of brain CT reading in suspected AIS patients. We hope this helps to minimize malpractice and a delayed diagnosis, and to improve competency-based medical education for MS and PC physicians

Geometry Based Approach to Obstacle Avoidance of Triomnidirectional Wheeled Mobile Robotic Platform

Mobile robots undergo a collision-free autonomous motion by using the information obtained from a suitable combination of multiple sensors of same or different families. These sensors are often configured around the chassis of the robotic platform. However, little to no information is available as to how these sensors are configured on mobile robotic platforms and how many of these sensors to place on such platforms. Instead, an empirical approach is adopted. That is, the number of sensors of the same family or any type as well as combination of sensors for detecting obstacles is determined by experiment or information obtained from external sensors. This approach is often seen to be iterative and time consuming. In this paper, an approach for determining the minimum number of sensors and their spacing on the robotic platform is proposed so that mobile robots undergo collision-free motion. The effectiveness of the developed approach is experimentally tested by examining the obstacle avoidance capability of the triomnidirectional wheeled robotic platform based on a motion triggering signal obtained from a skirt of ultrasonic sensors only. It was observed that the newly developed approach allows this robotic platform to avoid obstacles effectively

Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data

Recently, 6D pose estimation methods have shown robust performance on highly cluttered scenes and different illumination conditions. However, occlusions are still challenging, with recognition rates decreasing to less than 10% for half-visible objects in some datasets. In this paper, we propose to use top-down visual attention and color cues to boost performance of a state-of-the-art method on occluded scenarios. More specifically, color information is employed to detect potential points in the scene, improve feature-matching, and compute more precise fitting scores. The proposed method is evaluated on the Linemod occluded (LM-O), TUD light (TUD-L), Tejani (IC-MI) and Doumanoglou (IC-BIN) datasets, as part of the SiSo BOP benchmark, which includes challenging highly occluded cases, illumination changing scenarios, and multiple instances. The method is analyzed and discussed for different parameters, color spaces and metrics. The presented results show the validity of the proposed approach and their robustness against illumination changes and multiple instance scenarios, specially boosting the performance on relatively high occluded cases. The proposed solution provides an absolute improvement of up to 30% for levels of occlusion between 40% to 50%, outperforming other approaches with a best overall recall of 71% for the LM-O, 92% for TUD-L, 99.3% for IC-MI and 97.5% for IC-BIN

Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data

Recently, 6D pose estimation methods have shown robust performance on highly cluttered scenes and different illumination conditions. However, occlusions are still challenging, with recognition rates decreasing to less than 10% for half-visible objects in some datasets. In this paper, we propose to use top-down visual attention and color cues to boost performance of a state-of-the-art method on occluded scenarios. More specifically, color information is employed to detect potential points in the scene, improve feature-matching, and compute more precise fitting scores. The proposed method is evaluated on the Linemod occluded (LM-O), TUD light (TUD-L), Tejani (IC-MI) and Doumanoglou (IC-BIN) datasets, as part of the SiSo BOP benchmark, which includes challenging highly occluded cases, illumination changing scenarios, and multiple instances. The method is analyzed and discussed for different parameters, color spaces and metrics. The presented results show the validity of the proposed approach and their robustness against illumination changes and multiple instance scenarios, specially boosting the performance on relatively high occluded cases. The proposed solution provides an absolute improvement of up to 30% for levels of occlusion between 40% to 50%, outperforming other approaches with a best overall recall of 71% for the LM-O, 92% for TUD-L, 99.3% for IC-MI and 97.5% for IC-BIN

A Comprehensive Design of Six-Axis Force/Moment Sensor

Strain gage type six-axis force/moment (F/M) sensors have been largely studied and implemented in industrial applications by using an external data acquisition board (DAQ). The use of external DAQs will ill-affect accuracy and crosstalk due to the possibility of voltage drop through the wire length. The most recent research incorporated DAQ within a relatively small F/M sensor, but only for sensors of the capacitance and optical types. This research establishes the integration of a high-efficiency DAQ on six-axis F/M sensor with a revolutionary arrangement of 32 strain gages. The updated structural design was optimized using the sequential quadratic programming method and validated using Finite Element Analysis (FEA). A new, integrated DAQ system was designed, tested, and compared to commercial DAQ systems. The proposed six-axis F/M sensor was examined with the calibrated jig. The results show that the measurement error and crosstalk have been significantly reduced to 1.15% and 0.68%, respectively, the best published combination at this moment

Robotic Label Applicator: Design, Development and Visual Servoing Based Control

Use of robotic arms and computer vision in manufacture, and assembly process are getting more interest as flexible customization is becoming priority over mass production as frontier industry practice. In this paper an innovative label applicator as end of arm tooling (EOAT) capable of dispensing and applying label stickers of various dimensions to a product is designed, fabricated and tested. The system incorporates a label dispenserapplicator and had eye-in-hand camera system, attached to 6-dof robot arm can autonomously apply a label sticker to the target position on a randomly placed product. Employing multiple advantages from different knowledge basis, mechanism design and vision based automatic control, offers this system distinctive efficiency as well as flexibility to change in manufacturing and assembly process with time and cost saving