Optoranger: A 3D pattern matching method for bin picking applications

Sansoni, Giovanna; Bellandi, Paolo; Leoni, Fabio; Docchio, Franco

doi:10.1016/j.optlaseng.2013.07.014

Cited by 19 publications

(20 citation statements)

References 17 publications

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“…As shown in these two images, both the foot and the ground are well visible; in addition, the corresponding point clouds are well separated during the swing, while they are connected to each other during the stance. By suitably elaborating these images, it was possible to cluster the feet and the ground and, hence, to distinguish the swing from the stance [42]; then, following an approach similar to the one presented in Section VIII for the analysis of the dummy body segments, the PCA was used to estimate the angle at the ankle. The result of this analysis is shown in Fig.…”

Section: Testing the Picoflexx For Gait Analysismentioning

confidence: 99%

Performance Analysis of the PMD Camboard Picoflexx Time-of-Flight Camera for Markerless Motion Capture Applications

Pasinetti

Hassan

Eberhardt

et al. 2019

IEEE Trans. Instrum. Meas.

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The PMD Camboard Picoflexx Time-of-Flight (ToF) camera is evaluated against the Microsoft Kinect V2 to assess its performance in the context of markerless motion capture system for human body kinematics measurements. Various error sources such as the warm-up time, the depth distortion, the amplitude related error, the signal-to-noise ratio, and limitations such as their dependence on the illumination pattern and on the target distance, are studied and compared. The Picoflexx device is also compared to the Kinect V2 in relation to the quality of shape reconstructions, to assess its adequateness in modeling human body segments, and human body kinematics measurements. The final result of this paper is definitely useful to the research community, demonstrating that, even if the Picoflexx performs lower than the Kinect concerning the measurement performances, its behavior in estimating the volume of the body segments, the angles at the joints for human body kinematics, and the angle at the ankle in assisted walking applications is definitely satisfactory. These results are extremely significant to obtain accurate estimates of the parameters of the human body models in markerless motion capture applications, especially in laboratory-free environments, where compactness, lightweight, wireless connection, and low-power consumption are of outmost importance.

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Section: Testing the Picoflexx For Gait Analysismentioning

confidence: 99%

Performance Analysis of the PMD Camboard Picoflexx Time-of-Flight Camera for Markerless Motion Capture Applications

Pasinetti

Hassan

Eberhardt

et al. 2019

IEEE Trans. Instrum. Meas.

Self Cite

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“…Most previous attempts on a systems approach to bin-picking mainly focussed on the perception problem [25,24,23,21,4,20,19,18,16,15,14], while assuming accurate robot grasping. However, model inaccuracies and sensor uncertainties make it difficult for a majority of the perception algorithms to provide reliable object recognition and localization estimates, thereby affecting overall bin-picking performance.…”

Section: Perception For Robotic Bin-pickingmentioning

confidence: 99%

Addressing perception uncertainty induced failure modes in robotic bin-picking

Kaipa

Kankanhalli-Nagendra

Kumbla

et al. 2016

Robotics and Computer-Integrated Manufacturing

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We present a comprehensive approach to handle perception uncertainty to reduce failure rates in robotic bin-picking. Our focus is on mixed-bins. We identify the main failure modes at various stages of the bin-picking task and present methods to recover from them. If uncertainty in part detection leads to perception failure, then human intervention is invoked. Our approach estimates the confidence in the part match provided by an automated perception system, which is used to detect perception failures. Human intervention is also invoked if uncertainty in estimated part location and orientation leads to a singulation planning failure. We have developed a user interface that enables remote human interventions when necessary. Finally, if uncertainty in part posture in the gripper leads to failure in placing the part with the desired accuracy, sensor-less fine-positioning moves are used to correct the final placement errors. We have developed a fine-positioning planner with a suite of fine-motion strategies that offer different tradeoffs between completion time and postural accuracy at the destination. We report our observations from system characterization experiments with a dual-armed Baxter robot, equipped with a Ensenso three-dimensional camera, to perform bin-picking on mixed-bins.

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“…The adequate implementation of robotic manipulation tasks necessitates the accurate estimation of the 6 DoF pose of the testing object (Kouskouridas, Amanatiadis, & Gasteratos, 2011;Kouskouridas, Charalampous, & Gasteratos, 2014;Popovic et al, 2010;Sansoni et al, 2014). The simplicity along with facile training sessions render template matching methods as one of the most widely used solutions for object detection tasks (Ferrari, Tuytelaars, & Van Gool, 2006;Hinterstoisser et al, 2011;Ma, Chung, & Burdick, 2011;Rios-Cabrera & Tuytelaars, 2013;Tejani et al, 2014).…”

Section: Pose Estimationmentioning

confidence: 99%

“…Severe occlusions, foreground clutter and large scale changes are among the cascading issues that put additional barriers to this challenging problem. Liu et al (2012) presented a chamfer matching-based solution that extract depth edges via a multi-flash camera, while Sansoni, Bellandi, Leoni, and Docchio (2014) showed how a laser source scanning architecture can facilitate accurate pose estimation. In Buchholz, Kubus, Weidauer, Scholz, and Wahl (2014) inertial and visual data are fused to calculate grasp poses of testing objects (Kuo, Su, Lai, & Wu, 2014).…”

Section: Introductionmentioning

confidence: 99%