Author name cluster

Dominik Joho

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

ICRA Conference 2024 Conference Paper

Neural Implicit Swept Volume Models for Fast Collision Detection

Dominik Joho
Jonas Schwinn
Kirill Safronov

Collision detection is one of the most time-consuming operations during motion planning. Thus, there is an increasing interest in exploring machine learning techniques to speed up collision detection and sampling-based motion planning. A recent line of research focuses on utilizing neural signed distance functions of either the robot geometry or the swept volume of the robot motion. Building on this, we present a novel neural implicit swept volume model to continuously represent arbitrary motions parameterized by their start and goal configurations. This allows to quickly compute signed distances for any point in the task space to the robot motion. Further, we present an algorithm combining the speed of the deep learning-based signed distance computations with the strong accuracy guarantees of geometric collision checkers. We validate our approach in simulated and real-world robotic experiments, and demonstrate that it is able to speed up a commercial bin picking application.

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IROS Conference 2019 Conference Paper

Active SLAM using Connectivity Graphs as Priors

Alberto Soragna
Marco Baldini
Dominik Joho
Rainer Kümmerle
Giorgio Grisetti

Mobile robots can be considered completely autonomous if they embed active algorithms for Simultaneous Localization And Mapping (SLAM). This means that the robot is able to autonomously, or actively, explore and create a reliable map of the environment, while simultaneously estimating its pose. In this paper, we propose a novel framework to robustly solve the active SLAM problem, in scenarios in which some prior information about the environment is available in the form of a topo-metric graph. This information is typically available or can be easily developed in industrial environments, but it is usually affected by uncertainties. In particular, the distinguishing features of our approach are: the inclusion of prior information for solving the active SLAM problem; the exploitation of this information to pursue active loop closure; the on-line correction of the inconsistencies in the provided data. We present some experiments, that are performed in different simulated environments: the results suggest that our method improves on state-of-the-art approaches, as it is able to deal with a wide variety of possibly large uncertainties.

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ICRA Conference 2010 Conference Paper

Searching for objects: Combining multiple cues to object locations using a maximum entropy model

Dominik Joho
Wolfram Burgard

In this paper, we consider the problem of how background knowledge about usual object arrangements can be utilized by a mobile robot to more efficiently find an object in an unknown environment. We decompose the action selection problem during the search into two parts. First, we compute a belief over the location of the object and subsequently use the belief to select the next target location the robot should visit. For the inference part, we utilize a maximum entropy model which models the conditional distribution over possible locations of the target object given the observations made so far. The model is based on co-occurrences of objects and object attributes in different spatial contexts. The parameters are learned by maximizing the data likelihood using gradient ascent. We evaluate our approach by simulated search runs based on data obtained from different real-world environments. Our results show a significant improvement over a standard search technique which does not employ domain-specific background knowledge.

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ICRA Conference 2009 Conference Paper

Modeling RFID signal strength and tag detection for localization and mapping

Dominik Joho
Christian Plagemann
Wolfram Burgard

In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. We present a novel sensor model which can be utilized for localizing RFID tags and for tracking a mobile agent moving through an RFID-equipped environment. The proposed probabilistic sensor model characterizes the received signal strength indication (RSSI) information as well as the tag detection events to achieve a higher modeling accuracy compared to state-of-the-art models which deal with one of these aspects only. We furthermore propose a method that is able to bootstrap such a sensor model in a fully unsupervised fashion. Real-world experiments demonstrate the effectiveness of our approach also in comparison to existing techniques.

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IROS Conference 2005 Conference Paper

Integrating vision and speech for conversations with multiple persons

Maren Bennewitz
Felix Faber
Dominik Joho
Michael Schreiber
Sven Behnke

An essential capability for a robot designed to interact with humans is to show attention to the people in its surroundings. To enable a robot to involve multiple persons into interaction requires the maintenance of an accurate belief about the people in the environment. In this paper, we use a probabilistic technique to update the knowledge of the robot based on sensory input. In this way, the robot is able to reason about the uncertainty in its belief about people in the vicinity and is able to shift its attention between different persons. Even people who are not the primary conversational partners are included into the interaction. In practical experiments with a humanoid robot, we demonstrate the effectiveness of our approach.

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