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175
Learning motion patterns of people for compliant robot motion
, 2004
"... Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve ..."
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Cited by 105 (3 self)
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Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. In this paper we propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders are clustered using the expectation maximization algorithm. Based on the result of the clustering process, we derive a hidden Markov model that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot. We present several experiments carried out in different environments with a mobile robot equipped with a laser-range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict positions of persons, and can be used to improve the navigation behavior of a mobile robot.
Using boosted features for the detection of people in 2d range data
- IN PROC. IEEE INTL. CONF. ON ROBOTICS AND AUTOMATION (ICRA’07
, 2007
"... This paper addresses the problem of detecting people in two dimensional range scans. Previous approaches have mostly used pre-defined features for the detection and tracking of people. We propose an approach that utilizes a supervised learning technique to create a classifier that facilitates the ..."
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Cited by 93 (13 self)
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This paper addresses the problem of detecting people in two dimensional range scans. Previous approaches have mostly used pre-defined features for the detection and tracking of people. We propose an approach that utilizes a supervised learning technique to create a classifier that facilitates the detection of people. In particular, our approach applies AdaBoost to train a strong classifier from simple features of groups of neighboring beams corresponding to legs in range data. Experimental results carried out with laser range data illustrate the robustness of our approach even in cluttered office environments.
Monte Carlo Filtering for Multi-Target Tracking and Data Association
- IEEE Transactions on Aerospace and Electronic Systems
, 2004
"... In this paper we present Monte Carlo methods for multi-target tracking and data association. The methods are applicable to general non-linear and non-Gaussian models for the target dynamics and measurement likelihood. We provide efficient solutions to two very pertinent problems: the data associat ..."
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Cited by 86 (5 self)
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In this paper we present Monte Carlo methods for multi-target tracking and data association. The methods are applicable to general non-linear and non-Gaussian models for the target dynamics and measurement likelihood. We provide efficient solutions to two very pertinent problems: the data association problem that arises due to unlabelled measurements in the presence of clutter, and the curse of dimensionality that arises due to the increased size of the state-space associated with multiple targets. We develop a number of algorithms to achieve this. The first, which we will refer to as the Monte Carlo Joint Probabilistic Data Association Filter (MC-JPDAF), is a generalisation of the strategy proposed in [1], [2]. As is the case for the JPDAF, the distributions of interest are the marginal filtering distributions for each of the targets, but these are approximated with particles rather than Gaussians. We also develop two extensions to the standard particle filtering methodology for tracking multiple targets. The first, which we will refer to as the Sequential Sampling Particle Filter (SSPF), samples the individual targets sequentially by utilising a factorisation of the importance weights. The second, which we will refer to as the Independent Partition Particle Filter (IPPF), assumes the associations to be independent over the individual targets, leading to an efficient componentwise sampling strategy to construct new particles. We evaluate and compare the proposed methods on a challenging synthetic tracking problem.
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 ..."
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Cited by 58 (11 self)
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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 ..."
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Cited by 56 (3 self)
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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
Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities
- In Proc. IEEE International Conference on Robotics and Automation (ICRA
, 2008
"... Abstract — We present an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, and a multi-hypothesis data association strategy. People are defined as high-level tracks consisting of two ..."
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Cited by 42 (12 self)
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Abstract — We present an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, and a multi-hypothesis data association strategy. People are defined as high-level tracks consisting of two legs that are found with little model knowledge. We extend the data association so that it explicitly handles track occlusions in addition to detections and deletions. Additionally, we adapt the corresponding probabilities in a situation-dependent fashion so as to reflect the fact that legs frequently occlude each other. Experimental results carried out with a mobile robot illustrate that our approach can robustly and efficiently track multiple people even in situations of high levels of occlusion. I.
McMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Abstract—In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequate ..."
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Cited by 36 (1 self)
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Abstract—In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC. Index Terms—Markov chain Monte Carlo, QR factorization, updating, downdating, Rao-Blackwellized, particle filter, multitarget tracking, merged measurements, linear least squares, laser range scanner.
HERB: a home exploring robotic butler
, 2010
"... We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering object ..."
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Cited by 36 (7 self)
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We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. We also
Ubiquitous networking robotics in urban settings
- In Proceedings of the IEEE/RSJ IROS Workshop on Network Robot Systems
"... Abstract- In this paper we will present the objectives of a ..."
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Cited by 32 (16 self)
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Abstract- In this paper we will present the objectives of a
Multisensor-Based Human Detection and Tracking for Mobile Service Robots
"... Abstract—One of fundamental issues for service robots is human–robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that imple ..."
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Cited by 31 (4 self)
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Abstract—One of fundamental issues for service robots is human–robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot’s camera, and the information is fused to the legs ’ position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments. Index Terms—Leg detection, people tracking, sensor fusion, service robotics, unscented Kalman filter (UKF). I.
