Christian Rink

Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects

This work focuses on autonomous surface reconstruction of small-scale objects with a robot and a 3D sensor. The aim is a high-quality surface model allowing for robotic applications such as grasping and manipulation. Our approach comprises the generation of next-best-scan (NBS) candidates and selection criteria, error minimization between scan patches and termination criteria. NBS candidates are iteratively determined by a boundary detection and surface trend estimation of the acquired model. To account for both a fast and high-quality model acquisition, that candidate is selected as NBS, which maximizes a utility function that integrates an exploration and a mesh-quality component. The modeling and scan planning methods are evaluated on an industrial robot with a high-precision laser striper system. While performing the new laser scan, data are integrated on-the-fly into both, a triangle mesh and a probabilistic voxel space. The efficiency of the system in fast acquisition of high-quality 3D surface models is proven with different cultural heritage, household and industrial objects.

The DLR MiroSurge - A robotic system for surgery

This video presents the in-house developed DLR MiroSurge robotic system for surgery. As shown, the system is suitable for both minimally invasive and open surgery. Essential part of the system is the MIRO robot: The soft robotics feature enables intuitive interaction with the robot.

Next-best-scan planning for autonomous 3D modeling

We present a next-best-scan (NBS) planning approach for autonomous 3D modeling. The system successively completes a 3D model from complex shaped objects by iteratively selecting a NBS based on previously acquired data. For this purpose, new range data is accumulated in-the-loop into a 3D surface (streaming reconstruction) and new continuous scan paths along the estimated surface trend are generated. Further, the space around the object is explored using a probabilistic exploration approach that considers sensor uncertainty. This allows for collision free path planning in order to completely scan unknown objects. For each scan path, the expected information gain is determined and the best path is selected as NBS. The presented NBS approach is tested with a laser striper system, attached to an industrial robot. The results are compared to state-of-the-art next-best-view methods. Our results show promising performance with respect to completeness, quality and scan time.

On the convergence of the Monte Carlo block design

The Monte Carlo method is recommended to propagate distributions through the equation of the measurand in the first supplement to the GUM. The result of this method is an approximation of the probability density function (PDF) representing the output quantity of the measurement. This approximation can be used to calculate all necessary statistical parameters such as the expectation value or the standard uncertainty of the measurand. For computational reasons, it is advisable to use a block-by-block evaluation, the Monte Carlo block design, when implementing this method. The statistical parameters of the measurand are then calculated using the statistical parameters of equal sized blocks. This paper demonstrates that this procedure delivers reasonable estimators for the true statistical parameters of the PDF representing the measurand.

Feature based particle filter registration of 3D surface models and its application in robotics

This work is focused on global registration of surface models such as homogeneous triangle meshes and point clouds. The investigated approach utilizes feature descriptors in order to assign correspondences between the data sets and to reduce complexity by considering only characteristic feature points. It is based on the decomposability of rigid motions into a rotation and a translation. The space of rotations is searched with a particle filter and scoring is performed by looking for clusters in the resulting sets of translations. We use features computed from homogeneous triangle meshes and point clouds that require low computation time. A major advantage of the approach proves to be the possible consideration of prior knowledge about the relative orientation. This is especially important when high noise levels produce deteriorated features that are hard to match correctly. Comparisons to existing algorithms show the methods competitiveness, and results in robotic applications with different sensor types are presented.

Monte Carlo Registration and Its Application with Autonomous Robots

This work focuses on Monte Carlo registration methods and their application with autonomous robots. A streaming and an offline variant are developed, both based on a particle filter. The streaming registration is performed in real-time during data acquisition with a laser striper allowing for on-the-fly pose estimation. Thus, the acquired data can be instantly utilized, for example, for object modeling or robot manipulation, and the laser scan can be aborted after convergence. Curvature features are calculated online and the estimated poses are optimized in the particle weighting step. For sampling the pose particles, uniform, normal, and Bingham distributions are compared. The methods are evaluated with a high-precision laser striper attached to an industrial robot and with a noisy Time-of-Flight camera attached to service robots. The shown applications range from robot assisted teleoperation, over autonomous object modeling, to mobile robot localization.

On-the-fly particle filter registration for laser data

This work is focused on streaming particle filter registration of surface models such as homogeneous triangle meshes and point clouds. Part of the approach is a streaming curvature feature calculation. The investigated approach utilizes a particle filter to incrementally update pose estimates during data acquisition. The method is evaluated in real data experiments with a high-precision laser striper system attached to an industrial robot. During the laser scan, the data is integrated on-the-fly in order to calculate features and based on these to estimate the objects pose. Experiments show the methods competitiveness in accuracy and reliability compared to state-of-the-art offline algorithms.

Optimal setup of the DLR MiroSurge telerobotic system for minimally invasive surgery

This video presents the complete procedure for the optimal setup of the DLR MiroSurge telerobotic system for minimally invasive surgery. Two key features are implemented. First, optimization algorithms preoperatively determine several setups that are then rated and selected by the surgeon. Second, the intraoperative situation is taken into account. The newly developed VR-Map device together with fast registration and optimization algorithms enable a quick procedure to assure the optimal patient-specific setup of the robotic system.

Improving object orientation estimates by considering multiple viewpoints

This article describes a probabilistic approach for improving the accuracy of general object pose estimation algorithms. We propose a histogram filter variant that uses the exploration capabilities of robots, and supports active perception through a next-best-view proposal algorithm. For the histogram-based fusion method we focus on the orientation of the 6 degrees of freedom (DoF) pose, since the position can be processed with common filtering techniques. The detected orientations of the object, estimated with a pose estimator, are used to update the hypothesis of its actual orientation. We discuss the design of experiments to estimate the error model of a detection method, and describe a suitable representation of the orientation histograms. This allows us to consider priors about likely object poses or symmetries, and use information gain measures for view selection. The method is validated and compared to alternatives, based on the outputs of different 6 DoF pose estimators, using real-world depth images acquired using different sensors, and on a large synthetic dataset.

Streaming Monte Carlo Pose Estimation for Autonomous Object Modeling

This work contributes the optimization of a streaming pose estimation particle filter and its integration into an autonomous object modeling approach. The particle filter is advanced by an additional pose optimization in the particle weighting step. By integrating the method into the autonomous object modeling approach, the repositioning of objects is enabled, which is often necessary in order to acquire complete models. Experiments show that the usage of iterative closest point is too restrictive for general transformations. The used Monte Carlo method enables a robust pose estimation without loss of time and with high precision. Further, it is shown that the overall modeling results are improved clearly.