Dirk Hähnel

Experiences with an interactive museum tour-guide robot

This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence. At its heart, the software approach relies on probabilistic computation, on-line learning, and any-time algorithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot’s operation. Special emphasis is placed on the design of interactive capabilities that appeal to people’s intuition. The interface provides new means for human-robot interaction with crowds of people in public places, and it also provides people all around the world with the ability to establish a “virtual telepresence” using the Web. To illustrate our approach, results are reported obtained in mid-1997, when our robot “RHINO” was deployed for a period of six days in a densely populated museum. The empirical results demonstrate reliable operation in public environments. The robot successfully raised the museum’s attendance by more than 50%. In addition, thousands of people all over the world controlled the robot through the Web. We conjecture that these innovations transcend to a much larger range of application domains for service robots.

Mapping and localization with RFID technology

We analyze whether radio frequency identification (RFID) technology can be used to improve the localization of mobile robots and persons in their environment. In particular we study the problem of localizing RFID tags with a mobile platform that is equipped with a pair of RFID antennas. We present a probabilistic measurement model for RFID readers that allow us to accurately localize RFID tags in the environment. We also demonstrate how such maps can be used to localize a robot and persons in their environment. Finally, we present experiments illustrating that the computational requirements for global robot localization can be reduced strongly by fusing RFID information with laser data.

MINERVA: a second-generation museum tour-guide robot

This paper describes an interactive tour-guide robot, which was successfully exhibited in a Smithsonian museum. During its two weeks of operation, the robot interacted with thousands of people, traversing more than 44 km at speeds of up to 163 cm/sec. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction. It uses learning pervasively at all levels of the software architecture.

An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements

The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [J.-S. Gutman et al., 1999]. This paper presents a novel algorithm that combines Rao-Blackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches.

Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva

This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva’s software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. During 2 weeks of operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44 km at speeds of up to 163 cm/sec in the unmodified museum.

Gaussian Processes for Signal Strength-Based Location Estimation

Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, walls or people. In this paper we show how Gaussian processes can be used to generate a likelihood model for signal strength measurements. We also show how parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation. Experiments using WiFi indoor data and GSM cellphone connectivity demonstrate the superior performance of our approach.

Learning Compact 3D Models of Indoor and Outdoor Environments with a Mobile Robot

Abstract This paper presents an algorithm for full 3D shape reconstruction of indoor and outdoor environments with mobile robots. Data is acquired with laser range finders installed on a mobile robot. Our approach combines efficient scan matching routines for robot pose estimation with an algorithm for approximating environments using flat surfaces. On top of that, our approach includes a mesh simplification technique to reduce the complexity of the resulting models. In extensive experiments, our method is shown to produce accurate models of indoor and outdoor environments that compare favorably to other methods.

A system for volumetric robotic mapping of abandoned mines

This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To build consistent maps of large mines with many cycles, we describe an algorithm for estimating global correspondences and aligning robot paths. This algorithm enables us to recover consistent maps several hundreds of meters in diameter, without odometric information. We report results obtained in two mines, a research mine in Bruceton, PA, and an abandoned coal mine in Burgettstown, PA.

Map building with mobile robots in dynamic environments

The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or misalignments due to localization errors. In this paper we consider the problem of creating maps with mobile robots in dynamic environments. We present a new approach that interleaves mapping and localization with a probabilistic technique to identify spurious measurements. In several experiments we demonstrate that our algorithm generates accurate 2D and 3D in different kinds of dynamic indoor and outdoor environments. We also use our algorithm to isolate the dynamic objects and generate 3D representation of them.

Map building with mobile robots in populated environments

The problem of generating maps with mobile robots has received considerable attention over the past years. However, most of the approaches assume that the environment is static during the data-acquisition phase. In this paper we consider the problem of creating maps with mobile robots in populated environments. Our approach uses a probabilistic method to track multiple people and to incorporate the results of the tracking technique into the mapping process. The resulting maps are more accurate since corrupted readings are treated accordingly during the matching phase and since the number of spurious objects in the resulting maps is reduced. Our approach has been implemented and tested on real robot systems in indoor and outdoor scenarios. We present several experiments illustrating the capabilities of our approach to generate accurate 2D and 3D maps.