Author name cluster

Roderick de Nijs

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3 papers

1 author row

ICRA Conference 2013 Conference Paper

Route description interpretation on automatically labeled robot maps

Christian Landsiedel
Roderick de Nijs
Kolja Kühnlenz
Dirk Wollherr
Martin Buss

This paper presents an approach to combine automatic semantic place labeling of robot-generated maps with reasoning on human route descriptions. Enabling robots to understand human route descriptions can simplify HRI situations in household or industrial settings. However, solving this problem requires handling the ambiguity present in route descriptions and the possible unreliability of the semantic perception capabilities of the robot. We address this problem by absorbing these uncertainties in a probability distribution measuring the likelihood of the different interpretations (paths) of a given route description and selecting its MAP solution. The approach is evaluated on a dataset of route descriptions transcribed into a suitable representation using standard information retrieval metrics. These performance measurements indicate that the method can correctly interpret route descriptions even in challenging environments.

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IROS Conference 2012 Conference Paper

On-line semantic perception using uncertainty

Roderick de Nijs
Sebastian Ramos
Gemma Roig
Xavier Boix
Luc Van Gool
Kolja Kühnlenz

Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not be accurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance.

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ICRA Conference 2009 Conference Paper

New interval-based approach to determine the guaranteed singularity-free workspace of parallel robots

Jens Kotlarski
Roderick de Nijs
Houssem Abdellatif
Bodo Heimann

In the present paper we introduce an improved method to obtain the guaranteed singularity-free workspace of planar parallel kinematic machines. A geometric condition for the existence of singularities is extended to be used in interval analysis. Therefore, we eliminate the need of calculating the inconvenient interval form of the Jacobian's determinant. Hence, an appropriate description of the singularity-free workspace is obtained and the computational effort is reduced significantly. With the interval-based approach error sources, like manufacturing tolerances, can be considered. Consequently, regions that do not satisfy the proposed condition can be guaranteed not to have any type-two singularities. Several analysis examples demonstrate the efficiency of the proposed geometrical-based solver in comparison to existing solvers, i. e. based on the Jacobian's determinant.

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