Improving multitarget tracking using orientation estimates for sorting bulk materials

Pfaff, Florian; Kurz, Gerhard; Pieper, Christoph; Maier, Georg; Noack, Benjamin; Kruggel‐Emden, Harald; Gruna, Robin; Hanebeck, Uwe D.; Wirtz, S.; Scherer, Viktor; Längle, Thomas; Beyerer, Jürgen

doi:10.1109/mfi.2017.8170379

Cited by 8 publications

(13 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A positive number means that the particle arrived earlier than anticipated whereas a negative number indicates that it arrived later than expected. Adapted version of a plot from [16]. model with bias correction (CVBC), the constant acceleration model (CA), the constant acceleration model with limited velocity (CALV), and the identical acceleration model (IA).…”

Section: Temporal Deviationsmentioning

confidence: 99%

“…Due to large biases that would necessitate different axis limits for a proper visualization, we omit the CV model without bias correction. Results for this model are provided in [16,Ch. 4].…”

Section: Temporal Deviationsmentioning

confidence: 99%

“…In the current paper, we regard simulations of three different bulk materials and present motion models tailored to the bulk material sorting task. The models were first described in [16,Ch. 4].…”

Section: Introductionmentioning

confidence: 99%

“…The depiction of the tracking and prediction phases as regions is only intended to illustrate the concepts, and the actual boundaries may change in every time step. Adapted version of an illustration from[16].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Predictive tracking with improved motion models for optical belt sorting

Pfaff

Pieper

Maier

et al. 2020

At - Automatisierungstechnik

Self Cite

View full text Add to dashboard Cite

Optical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type.

show abstract

Section: Temporal Deviationsmentioning

confidence: 99%

Section: Temporal Deviationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Predictive tracking with improved motion models for optical belt sorting

Pfaff

Pieper

Maier

et al. 2020

At - Automatisierungstechnik

Self Cite

View full text Add to dashboard Cite

show abstract

“…This forces human workers to serve as pick-and-place filters for a nominally fully-automated process . Research on recycling automation does not address this issue, instead focusing on separating increasingly similar materials from each other, such as paper grade and particle separation [15,16].…”

Section: Introductionmentioning

confidence: 99%

Automated Recycling Separation Enabled by Soft Robotic Material Classification

Chin

Lipton

Yuen

et al. 2019

2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)

View full text Add to dashboard Cite

Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting. Soft robotics offers a natural solution as compliant robots require less computation to plan paths and grasp objects in a cluttered environment. However, most soft robots are not robust enough to handle the many sharp objects present in a recycling facility. In this work, we present a soft sensorized robotic gripper which is fully electrically driven and can detect the difference between paper, metal and plastic. By combining handed shearing auxetics with high deformation capacitive pressure and strain sensors, we present a new puncture resistant soft robotic gripper. Our materials classifier has 85% accuracy with a stationary gripper and 63% accuracy in a simulated recycling pipeline. This classifier works over a variety of objects, including those that would fool a purely vision-based system.

show abstract

Association Likelihoods for Directional Estimation

Pfaff

Hanebeck

2019

2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)

View full text Add to dashboard Cite

Improving multitarget tracking using orientation estimates for sorting bulk materials

Cited by 8 publications

References 24 publications

Predictive tracking with improved motion models for optical belt sorting

Predictive tracking with improved motion models for optical belt sorting

Automated Recycling Separation Enabled by Soft Robotic Material Classification

Association Likelihoods for Directional Estimation

Contact Info

Product

Resources

About