Results 1 - 10
of
150
Pedestrian localisation for indoor environments
- In Proceedings of the 10th international conference on Ubiquitous computing
, 2008
"... Location information is an important source of context for ubiquitous computing systems. This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial unit and with ..."
Abstract
-
Cited by 100 (2 self)
- Add to MetaCart
(Show Context)
Location information is an important source of context for ubiquitous computing systems. This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial unit and without knowledge of the user’s initial location. We show how to handle multiple floors and stairways, how to handle symmetry in the environment, and how to initialise the localisation algorithm using WiFi signal strength to reduce initial complexity. We evaluate the entire system experimentally, using an independent tracking system for ground truth. Our results show that we can track a user throughout a 8725 m 2 building spanning three floors to within 0.5 m 75 % of the time, and to within 0.73 m 95 % of the time. ACM Classification Keywords
Point-Based Value Iteration for Continuous POMDPs
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are restricted to discrete states, actions, and observations, but many real-world problems such as, for instance, robot na ..."
Abstract
-
Cited by 74 (4 self)
- Add to MetaCart
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are restricted to discrete states, actions, and observations, but many real-world problems such as, for instance, robot navigation, are naturally defined on continuous spaces. In this work, we demonstrate that the value function for continuous POMDPs is convex in the beliefs over continuous state spaces, and piecewise-linear convex for the particular case of discrete observations and actions but still continuous states. We also demonstrate that continuous Bellman backups are contracting and isotonic ensuring the monotonic convergence of value-iteration algorithms. Relying on those properties, we extend the PERSEUS algorithm, originally developed for discrete POMDPs, to work in continuous state spaces by representing the observation, transition, and reward models using Gaussian mixtures, and the beliefs using Gaussian mixtures or particle sets. With these representations, the integrals that appear in the Bellman backup can be computed in closed form and, therefore, the algorithm is computationally feasible. Finally, we further extend PERSEUS to deal with continuous action and observation sets by designing effective sampling approaches.
A practical, decision-theoretic approach to multi-robot mapping and exploration
- In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS
, 2003
"... An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the rela ..."
Abstract
-
Cited by 69 (5 self)
- Add to MetaCart
(Show Context)
An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual robots. This paper presents a novel approach to multi-robot map merging under global uncertainty about the robot’s relative locations. Our approach uses an adapted version of particle filters to estimate the position of one robot in the other robot’s partial map. The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach. We show how to seamlessly integrate this approach into a decision-theoretic multi-robot coordination strategy. The experiments show that our sample-based technique can reliably find good hypotheses for map matches. Furthermore, we present results obtained with two robots successfully merging their maps using the decision-theoretic rendezvous strategy. 1
Voronoi tracking: location estimation using sparse and noisy sensor data,”
- in 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems. (IROS
, 2003
"... Abstract Tracking the activity of people in indoor environments has gained considerable attention in the robotics community over the last years. Most of the existing approaches are based on sensors which allow to accurately determine the locations of people but do not provide means to distinguish b ..."
Abstract
-
Cited by 58 (11 self)
- Add to MetaCart
Abstract Tracking the activity of people in indoor environments has gained considerable attention in the robotics community over the last years. Most of the existing approaches are based on sensors which allow to accurately determine the locations of people but do not provide means to distinguish between different persons. In this paper we propose a novel approach to tracking moving objects and their identity using noisy, sparse information collected by id-sensors such as infrared and ultrasound badge systems. The key idea of our approach is to use particle filters to estimate the locations of people on the Voronoi graph of the environment. By restricting particles to a graph, we make use of the inherent structure of indoor environments. The approach has two key advantages. First, it is by far more efficient and robust than unconstrained particle filters. Second, the Voronoi graph provides a natural discretization of human motion, which allows us to apply unsupervised learning techniques to derive typical motion patterns of the people in the environment. Experiments using a robot to collect ground-truth data indicate the superior performance of Voronoi tracking. Furthermore, we demonstrate that EMbased learning of behavior patterns increases the tracking performance and provides valuable information for highlevel behavior recognition.
Map-based multiple model tracking of a moving object
- Proceedings of eight RoboCup International Symposium
, 2004
"... Abstract. In this paper we propose an approach for tracking a moving target using Rao-Blackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete ..."
Abstract
-
Cited by 56 (3 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper we propose an approach for tracking a moving target using Rao-Blackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete states represent the non-linear parts of the state estimation problem. In the context of target tracking, these are the non-linear motion of the observing platform and the different motion models for the target. Using this representation, we show how to reason about physical interactions between the observing platform and the tracked object, as well as between the tracked object and the environment. The approach is implemented on a four-legged AIBO robot and tested in the context of ball tracking in the RoboCup domain. 1
Distributed multi-robot exploration and mapping
- In Proceedings of the IEEE
, 2006
"... Abstract — Efficient exploration of unknown environments is a fundamental problem in mobile robotics. In this paper we present an approach to distributed multi-robot mapping and exploration. Our system enables teams of robots to efficiently explore environments from different, unknown locations. In ..."
Abstract
-
Cited by 53 (1 self)
- Add to MetaCart
(Show Context)
Abstract — Efficient exploration of unknown environments is a fundamental problem in mobile robotics. In this paper we present an approach to distributed multi-robot mapping and exploration. Our system enables teams of robots to efficiently explore environments from different, unknown locations. In order to ensure consistency when combining their data into shared maps, the robots actively seek to verify their relative locations. Using shared maps, they coordinate their exploration strategies so as to maximize the efficiency of exploration. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust. The maps generated by our approach are consistently more accurate than those generated by manually measuring the locations and extensions of rooms and objects. I.
Real-time slam with octree evidence grids for exploration in underwater tunnels
- Journal of Field Robotics
, 2007
"... At 110m in diameter and over 350m in depth, the cenote Zacatón in central Mexico is a unique flooded sinkhole. A platform for conducting preliminary sonar tests is tethered in place. We describe a Simultaneous Localization and Mapping (SLAM) method for a hovering underwater vehicle that will explore ..."
Abstract
-
Cited by 44 (6 self)
- Add to MetaCart
(Show Context)
At 110m in diameter and over 350m in depth, the cenote Zacatón in central Mexico is a unique flooded sinkhole. A platform for conducting preliminary sonar tests is tethered in place. We describe a Simultaneous Localization and Mapping (SLAM) method for a hovering underwater vehicle that will explore underwater caves and tunnels, a true three dimensional (3D) environment. Our method consists of a Rao-Blackwellized particle filter with a 3D evidence grid map representation. We describe a procedure for dynamically adjusting the number of particles to provide real-time performance. We also describe how we adjust the particle filter prediction step to accommodate sensor degradation or failure. We present an efficient octree data structure which makes it feasible to maintain the hundreds of maps needed by the particle filter to accurately
Practical vision-based Monte Carlo localization on a legged robot
- in IEEE International Conference on Robotics and Automation
, 2005
"... Abstract — Mobile robot localization, the ability of a robot to determine its position and orientation in a global frame of reference, continues to be a major research focus in robotics. In most past cases, such localization has a� been studied on wheeled robots with range-finding sensors such as so ..."
Abstract
-
Cited by 43 (20 self)
- Add to MetaCart
(Show Context)
Abstract — Mobile robot localization, the ability of a robot to determine its position and orientation in a global frame of reference, continues to be a major research focus in robotics. In most past cases, such localization has a� been studied on wheeled robots with range-finding sensors such as sonar or lasers. In this paper, we consider the more challenging scenario of a legged robot localizing with limited-field-of-view vision as the primary sensory input. We begin with a baseline implementation adapted from the literature that provides a reasonable level of competence, but that exhibits some weaknesses in realworld tests. We propose a series of practical enhancements designed to improve the robot’s sensory and actuator models that enable our robots to achieve improvement in localization accuracy over the baseline implementation, and even more dramatic improvements when the robot is subjected to large unexpected movements. These enhancements are each individually straightforward, and they do not change the basic particle filtering approach. But together they provide a practical guide for avoiding potential pitfalls when implementing it on vision-based and/or legged robots. Our complete localization system is fully implemented on the Sony ERS-7 robot platform. We present extensive empirical results, both in simulation and on the physical robots, isolating the impacts of our contributions.
Real-time vision on a mobile robot platform
- in: The IEEE International Conference on Intelligent Robots and Systems, IROS
, 2005
"... Under review- not for citation Computer vision is a broad and significant ongoing research challenge, even when performed on an individual image or on streaming video from a high-quality stationary camera with abundant computational resources. When faced with streaming video from a lower-quality, ra ..."
Abstract
-
Cited by 34 (13 self)
- Add to MetaCart
(Show Context)
Under review- not for citation Computer vision is a broad and significant ongoing research challenge, even when performed on an individual image or on streaming video from a high-quality stationary camera with abundant computational resources. When faced with streaming video from a lower-quality, rapidly, jerkily-moving camera and limited computational resources, the challenge only increases. In this paper we present our implementation of a real-time vision system on a mobile robot platform that uses a camera image as the primary sensory input. The constraints imposed on the problem as a result of having to perform all processing, including segmentation and object detection, in real-time onboard the robot eliminate the possibility of using some stateof-the-art methods that otherwise might apply. We present the methods that we developed to achieve a practical vision system within these constraints. Our approach is fully implemented and tested on a team of Sony AIBO robots, enabling them to place among the top finishers at an annual international robot soccer competition. 1.
Bayesian color estimation for adaptive vision-based robot localization
- in IROS
, 2004
"... Abstract — In this article we introduce a hierarchical Bayesian model to estimate a set of colors with a mobile robot. Estimating colors is particularly important if objects in an environment can only be distinguished by their color. Since the appearance of colors can change due to variations in the ..."
Abstract
-
Cited by 27 (0 self)
- Add to MetaCart
(Show Context)
Abstract — In this article we introduce a hierarchical Bayesian model to estimate a set of colors with a mobile robot. Estimating colors is particularly important if objects in an environment can only be distinguished by their color. Since the appearance of colors can change due to variations in the lighting condition, a robot needs to adapt its color model to such changes. We propose a two level Gaussian model in which the lighting conditions are estimated at the upper level using a switching Kalman filter. A hierarchical Bayesian technique learns Gaussian priors from data collected in other environments. Furthermore, since estimation of the color model depends on knowledge of the robot’s location, we employ a Rao-Blackwellised particle filter to maintain a joint posterior over robot positions and lighting conditions. We evaluate the technique in the context of the RoboCup AIBO league, where a legged AIBO robot has to localize itself in an environment similar to a soccer field. Our experiments show that the robot can localize under different lighting conditions and adapt to changes in the lighting condition, for example, due to a light being turned on or off. I.
