nils-herrmann/clean_scirepeval huggingface dataset:
Markov Localization for Mobile Robots in Dynamic Environments

Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described here has been implemented and tested in several real-world applications of mobile robots, including the deployments of two mobile robots as interactive museum tour-guides.
Mosaicing a Large Number of Widely Dispersed, Noisy, and Distorted Images: A Bayesian Approach

In this paper we extend existing mosaicing algorithms to deal with image sequences that are captured over a wide spatial area, exhibit large geometric and photometric distortions, and contain significant additive noise and other contaminations, such as light reflections. The paper focuses on three main contributions. (1) We extend the camera model used for mosaicing to deal, not only with geometric lens distortion, but with vignetting and permanent occluders as well. (2) We introduce a novel method for global image alignment based on a technique from the robotics literature, together with a novel optimization strategy, the folding algorithm, to guarantee global convergence. (3) We utilize techniques developed in the super-resolution literature for restoration of the final image mosaic from the contaminated input images. The estimation of camera parameters, the global alignment and the final mosaic are all derived within a unifying Bayesian framework, starting with the single objective of obtaining the maximum a posteriori estimate of the final mosaic, given the input images. Our approach is illustrated with results on a complex and challenging image sequence obtained from a state of the art robotics application.
Collaborative Multi-robot Localization

This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot’s belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots. The robots detect each other and estimate their relative locations based on computer vision and laser range-finding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.
Spontaneous, short-term interaction with mobile robots

This paper considers a specific type of interaction: short-term and spontaneous interaction with crowds of people. Such patterns of interactions are found when service robots operate in public places, for example information kiosks, receptionists, and tour-guide robots applications. We describe our approach to spontaneous short-term interaction: a robot designed to be a believable social agent. The approach has been implemented using a mobile robot with a motorized face as focal point for interaction, an architecture that suggests the robot has moods, and a method for learning how to interact with people. Our system was recently deployed at a Smithsonian museum in Washington, DC. During a two week period it interacted with thousands of people. The robot's interactive capabilities were essential for its high on-task performance, and thus its practical success.
Automated Learning and Discovery State-of-the-Art and Research Topics in a Rapidly Growing Field

This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision making based on data. This report is organized in two parts. The first part (pages 1-6) summarizes the CONALD meeting and highlights its main outcomes, beyond the individual workshop level. The second part (pages 7-30) summarize the results obtained in the individual workshops, discussing in depth promising research topics. This report is available through the Web at http://www.cs.cmu.edu/ conald.
A Monte Carlo Algorithm for Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots, using computer vision and laser range finding for detecting each other and estimating each other's relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.
A Bayesian Approach to Landmark Discovery and Active Perception in Mobile Robot Navigation

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Over the past few years, localization based on landmarks has become increasingly popular. Virtually all existing approaches to landmark-based navigation, however, rely on the human designer to decide what constitutes appropriate landmarks. This paper presents an approach that enables mobile robots to select their landmarks by themselves. Landmarks are chosen based on their utility for localization. This is done by training neural network landmark detectors so as to minimize the a posteriori localization error that the robot is expected to make after querying its sensors. An empirical study illustrates that self-selected landmarks are superior to landmarks carefully selected by a human. The Bayesian approach is also applied to control the direction of the robot’s camera, and empirical data demonstrates the appropriateness of this approach for active perception. The author is also affiliated with the Computer Science Department III of the University of Bonn, Germany, where part of this research was carried out. This research is sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Defense Advanced Research Projects Agency (DARPA) under grant number F33615-93-1-1330. The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of NSF, Wright Laboratory or the United States Government.
Monte Carlo localization for mobile robots

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular the problems encountered are closely related to the type of representation used to represent probability densities over the robot's state space. Earlier work on Bayesian filtering with particle-based density representations opened up a new approach for mobile robot localization based on these principles. We introduce the Monte Carlo localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods,.
Probabilistic Methods for Mobile Robot Mapping

The problem of map building is the problem of determining the location of entities-of-interest in a global frame of reference. Over the last years, probabilistic methods have shown to be well suited for dealing with the uncertainties involved in mobile robot map building. In this paper we introduce a general probabilistic approach to concurrent mapping and localization. This method poses the mapping problem as a statistical maximum likelihood problem, and devises an efficient algorithm for search in likelihood space. We furthermore address the problem of using occupancy grid maps for path planning in highly dynamic environments. The approaches have been tested extensively and several experimental results are given in the paper.
Bayesian Network Induction via Local Neighborhoods

In recent years, Bayesian networks have become highly successful tool for diagnosis, analysis, and decision making in real-world domains. We present an efficient algorithm for learning Bayes networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a maximally consistent way. In contrast to the majority of work, which typically uses hill-climbing approaches that may produce dense and causally incorrect nets, our approach yields much more compact causal networks by heeding independencies in the data. Compact causal networks facilitate fast inference and are also easier to understand. We prove that under mild assumptions, our approach requires time polynomial in the size of the data and the number of nodes. A randomized variant, also presented here, yields comparable results at much higher speeds.
Learning Analytically and Inductively1

Learning is a fundamental component of intelligence, and a key consideration in designing cognitive architectures such as Soar [Laird et al., 1986]. This chapter considers the question of what constitutes an appropriate general-purpose learning mechanism. We are interested in mechanisms that might explain and reproduce the rich variety of learning capabilities of humans, ranging from learning perceptual-motor skills such as how to ride a bicycle, to learning highly cognitive tasks such as how to play chess.
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80×25 m illustrate the appropriateness of the approach.
Integrating Topological and Metric Maps for Mobile Robot Navigation: A Statistical Approach

The problem of concurrent mapping and localization has received considerable attention in the mobile robotics community. Existing approaches can largely be grouped into two distinct paradigms: topological and metric. This paper proposes a method that integrates both. It poses the mapping problem as a statistical maximum likelihood problem, and devises an efficient algorithm for search in likelihood space. It presents an novel mapping algorithm that integrates two phases: a topological and a metric mapping phase. The topological mapping phase solves a global position alignment problem between potentially indistinguishable, significant places. The subsequent metric mapping phase produces a fine-grained metric map of the environment in floating-point resolution. The approach is demonstrated empirically to scale up to large, cyclic, and highly ambiguous environments.
Position Estimation for Mobile Robots in Dynamic Environments

For mobile robots to be successful, they have to navigate safely in populated and dynamic environments. While recent research has led to a variety of localization methods that can track robots well in static environments, we still lack methods that can robustly localize mobile robots in dynamic environments, in which people block the robot's sensors for extensive periods of time or the position of furniture may change. This paper proposes extensions to Markov localization algorithms enabling them to localize mobile robots even in densely populated environments. Two different filters for determining the "believability" of sensor readings are employed. These filters are designed to detect sensor readings that are corrupted by humans or unexpected changes in the environment. The technique was recently implemented and applied as part of an installation, in which a mobile robot gave interactive tours to visitors of the "Deutsches Museum Bonn." Extensive empirical tests involving datasets recorded during peak traffic hours in the museum demonstrate that this approach is able to accurately estimate the robot's position in more than 98% of the cases even in such highly dynamic environments.
Template-Based Recognition of Pose and Motion Gestures On a Mobile Robot

For mobile robots to assist people in everyday life, they must be easy to instruct. This paper describes a gesture-based interface for human robot interaction, which enables people to instruct robots through easy-to-perform arm gestures. Such gestures might be static pose gestures, which involve only a specific configuration of the person's arm, or they might be dynamic motion gestures (such as waving). Gestures are recognized in real-time at approximate frame rate, using a hybrid approach that integrates neural networks and template matching. A fast, color-based tracking algorithm enables the robot to track and follow a person reliably through office environments with drastically changing lighting conditions. Results are reported in the context of an interactive clean-up task, where a person guides the robot to specific locations that need to be cleaned, and the robot picks up trash which it then delivers to the nearest trash-bin.
Finding landmarks for mobile robot navigation

Localization addresses the problem of determining the position of a mobile robot from sensor data. This paper presents an algorithm, called BaLL, which enables a mobile robot to learn a set of landmarks used in localization and to learn how to recognize them using artificial neural networks. BaLL is based on a statistical localization approach. It is applicable to a large variety of sensors and environments. Experiments with a mobile robot equipped with sonar sensors and a camera illustrate that BaLL identifies highly useful landmarks.
Learning to Learn: Introduction and Overview

Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications (see e.g., [Langley, 1992; Widrow et al., 1994]).
Jacobian Images of Super-Resolved Texture for Model-Based Motion Estimation and Tr

We present a Kalman filter based approach to pefom model-based motion estimation and tracking. Unlikeprevious approaches, the tracking process is not formulated as an SSD minimization problem, but is developed by using texture mapping as the measurement model in an extended Kalman filter During tracking, a super-resolved estimate of the texture present on the object or in the scene is obtained. A key result is the notion of Jacobian images, which can be viewed as a generalization of traditional gradient images, and represent the crucial computation in the tracking process, The approach is illustrated with three sample applications: full 30 tracking ofplanar surface patches, a projective surface tracker for uncalibrated camera scenarios, and a fast, KaImanJiltered version of mosaicking with detection of independently moving objects.
Learning to Locate an Object in 3D Space from a Sequence of Camera Images

This paper addresses the problem of determining an object’s 3D location from a sequence of camera images recorded by a mobile robot. The approach presented here allows people to “train” robots to recognize specific objects, by presenting it examples of the object to be recognized. A decision tree method is used to learn significant features of the target object from individual camera images. Individual estimates are integrated over time using Bayes rule, into a probabilistic 3D model of the robot’s environment. Experimental results illustrate that the method enables a mobile robot to robustly estimate the 3D location of objects from multiple camera images.
Monte Carlo Hidden Markov Models

We present a learning algorithm for hidden Markov models with continuous state and observation spaces. All necessary probability density functions are approximated using samples, along with density trees generated from such samples. A Monte Carlo version of Baum-Welch (EM) is employed to learn models from data, just as in regular HMM learning. Regularization during learning is obtained using an exponential shrinking technique. The shrinkage factor, which determines the effective capacity of the learning algorithm, is annealed down over multiple iterations of Baum-Welch, and early stopping is applied to select the right model. We prove that under mild assumptions, Monte Carlo Hidden Markov Models converge to a local maximum in likelihood space, just like conventional HMMs. In addition, we provide empirical results obtained in a gesture recognition domain, which illustrate the appropriateness of the approach in practice.
The Interactive Museum Tour-Guide Robot

This paper describes the software architecture of an autonomous tour-guideltutor robot. This robot was recently deployed in the "Deutsches Museum Bonn," were it guided hundreds of visitors through the museum during a six-day deployment period. The robot's control software integrates low-level probabilistic reasoning with high-level problem solving embedded in first order logic. A collection of software innovations, described in this paper, enabled the robot to navigate at high speeds through dense crowds, while reliably avoiding collisions with obstacles--some of which could not even be perceived. Also described in this paper is a user interface tailored towards non-expert users, which was essential for the robot's success in the museum. Based on these experiences, this paper argues that time is ripe for the development of AI-based commercial service robots that assist people in everyday life.
Towards exact localization without explicit localization with the generalized Voronoi graph

Sensor based exploration is a task which enables a robot to explore and map an unknown environment, using sensor information. The map used in this paper is the generalized Voronoi graph (GVG). The robot explores an unknown environment using an already developed incremental construction procedure to generate the GVG using sensor information. This paper presents some initial results which uses the GVG for robot localization, while mitigating the need to update encoder values. Experimental results verify the described work.
Markov Localization for Reliable Robot Navigation and People Detection

Localization is one of the fundamental problems in mobile robotics. Without knowledge about their position mobile robots cannot efficiently carry out their tasks. In this paper we present Markov localization as a technique for estimating the position of a mobile robot. The key idea of this technique is to maintain a probability density over the whole state space of the robot within its environment. This way our technique is able to globally localize the robot from scratch and even to recover from localization failures, a property which is essential for truly autonomous robots. The probabilistic framework makes this approach robust against approximate models of the environment as well as noisy sensors. Based on a fine-grained, metric discretization of the state space, Markov localization is able to incorporate raw sensor readings and does not require predefined landmarks. It also includes a filtering technique which allows to reliably estimate the position of a mobile robot even in densely populated environments. We furthermore describe, how the explicit representation of the density can be exploited in a reactive collision avoidance system to increase the robustness and reliability of the robot even in situations in which it is uncertain about its position. The method described here has been implemented and tested in several real-world applications of mobile robots including the deployments of two mobile robots as interactive museum tour-guides.
New Directions: When Robots Meet People

An ongoing revolution is taking place in mobile robotics. Robots that guide blind or mentally handicapped people, clean large office buildings and department stores, or assist people in recreational activities are clearly within reach. For many of those target domains, prototypes are readily available. The author discusses a case study of a mobile tour guide robot in a branch of the Deutsches Museum. The robot's task was to guide visitors through the museum, explaining to them a subset of the museum's exhibits. The project's major challenges are grouped into two categories: navigation and human-robot interaction.
Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge

Recently, there has been an increased interest in machine learning methods that transfer knowledge across multiple learning tasks and “learn to learn.” Such methods have repeatedly been found to outperform conventional, single-task learning algorithms when the learning tasks are appropriately related. To increase robustness of such approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to avoid the danger arising from tasks that are unrelated and thus potentially misleading.
Jacobian images of super-resolved texture maps for model-based motion estimation and tracking

We present a Kalman filter based approach to perform model-based motion estimation and tracking. Unlike previous approaches, the tracking process is not formulated as an SSD minimization problem, but is developed by using texture mapping as the measurement model in an extended Kalman filter. During tracking, a super-resolved estimate of the texture present on the object or in the scene is obtained. A key result is the notion of Jacobian images, which can be viewed as a generalization of traditional gradient images, and represent the crucial computation in the tracking process. The approach is illustrated with three sample applications: full 3D tracking of planar surface patches, a projective surface tracker for uncalibrated camera scenarios, and a fast, Kalman filtered version of mosaicking with detection of independently moving objects.