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173
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks
, 2002
"... A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robo ..."
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Cited by 279 (12 self)
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A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops stereo vision system, these landmarks are localized and robot ego-motion is estimated by least-squares minimization of the matched landmarks. Feature viewpoint variation and occlusion are taken into account by maintaining a view direction for each landmark. Experiments show that these visual landmarks are robustly matched, robot pose is estimated and a consistent three-dimensional map is built. As image features are not noise-free, we carry out error analysis for the landmark positions and the robot pose. We use Kalman filters to track these landmarks in a dynamic environment, resulting in a database map with landmark positional uncertainty.
Distributed multirobot localization
- IEEE Transactions on Robotics and Automation
, 2002
"... Abstract. This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman ..."
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Cited by 180 (20 self)
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Abstract. This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented. 1
Localization methods for a mobile robot in urban environments
- IEEE Transactions on Robotics
, 2004
"... Abstract — This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, an ..."
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Cited by 62 (1 self)
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Abstract — This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments. Index Terms — Mobile robots, localization, machine vision I.
Online self-calibration for mobile robots
- in IEEE International Conference on Robotics and Automation (ICRA
, 1999
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Elastic correction of dead-reckoning errors in map building
- In Intl. Conf. on Intelligent Robots and Systems
, 1998
"... Abstract—Map building is an important issue for all the applications in mobile robotics in which the environment is unknown and, in general, in order to have a robot exhibit a fully autonomous behavior. A major problem in map building is due to the imprecision of sensor measures. In this paper, we p ..."
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Cited by 37 (1 self)
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Abstract—Map building is an important issue for all the applications in mobile robotics in which the environment is unknown and, in general, in order to have a robot exhibit a fully autonomous behavior. A major problem in map building is due to the imprecision of sensor measures. In this paper, we propose a technique, called elastic correction, for correcting the dead-reckoning errors made during the exploration of an environment by a robot capable of identifying landmarks. Knowledge being acquired is modeled by a relational graph whose vertices and arcs represent, respectively, landmarks and routes. Elastic correction is based on an analogy between the graph modeling the environment and a mechanical structure: the map is regarded as a truss where each route is an elastic bar and each landmark a node. Errors are corrected as a result of the deformations induced from the forces arising within the structure as inconsistent measures are taken. The elasticity parameters characterizing the structure are used to model the uncertainty on odometry. The paper presents results from simulations showing the effectiveness of the method for reducing the overall metric error and proving its robustness with reference to topological errors and to unpredictable sensor errors. Index Terms—Error correction, mobile robotics, odometry. I.
Circumventing dynamic modeling: Evaluation of the error-state kalman applied to mobile robot localization
- In Proceedings of the 1999 IEEE International Conference on Robotics and Automation
, 1999
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The odometry error of a mobile robot with a synchronous drive system
- IEEE Trans. on Robotics and Automation Vol
"... Abstract—This paper presents an error modeling of an odometry system for a synchronous-drive system and a possible strategy for evaluating this error. The odometry error is modeled by introducing four parameters characterizing its systematic and nonsystematic components (translational and rotational ..."
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Cited by 34 (11 self)
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Abstract—This paper presents an error modeling of an odometry system for a synchronous-drive system and a possible strategy for evaluating this error. The odometry error is modeled by introducing four parameters characterizing its systematic and nonsystematic components (translational and rotational). The nonsystematic errors are expressed in terms of a covariance matrix, which depends on both the previous four parameters and the path followed by the mobile robot. In contrast to previous approaches which require assuming a particular path (straight or circular) in order to compute this covariance matrix, here general formulas are derived. We suggest a possible strategy for simultaneously estimating the four model parameters. As we will show, our strategy only requires measuring the change in the orientation and position between the initial and final configurations of the robot, related to suitable robot motions. In other words, it is unnecessary to know the actual path followed by the robot. We illustrate the proposed strategy by discussing the accuracy of the parameters estimation and by showing some experimental results obtained with the mobile robot Nomad150. Index Terms—Localization, odometry, robot navigation. I.
Development and experimental validation of an adaptive extended kalman filter for the localization of mobile robots
- IEEE Trans on robotics and automation, vol.15, No.2
, 1999
"... Abstract—A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The ..."
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Cited by 30 (1 self)
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Abstract—A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The performance of the filter is improved by an on line adjustment of the input and measurement noise covariances obtained by a suitably defined estimation algorithm. Index Terms—Adaptive filtering, localization systems, sensor fusion, wheeled mobile robots. I.
Stochastic Cloning: A generalized framework for processing relative state measurements
"... This paper introduces a generalized framework, termed "stochastic cloning," for processing relative state measurements within a Kalman filter estimator. The main motivation and application for this methodology is the problem of fusing displacement measurements with position estimates for m ..."
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Cited by 26 (15 self)
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This paper introduces a generalized framework, termed "stochastic cloning," for processing relative state measurements within a Kalman filter estimator. The main motivation and application for this methodology is the problem of fusing displacement measurements with position estimates for mobile robot localization. Previous approaches have ignored the developed interdependencies (cross-correlation terms) between state estimates of the same quantities at different time instants. By directly expressing relative state measurements in terms of previous and current state estimates, the effect of these cross-correlation terms on the estimation process is analyzed and considered during updates. Simulation and experimental results validate this approach.
Observability analysis for mobile robot localization
- in Proc. of the IEEE/RSJ Int. Conf. on Intel. Rob. and Sys
"... Abstract — In this paper the problem of localize two mobile robots is considered. The robots are equipped with proprioceptive sensors (like encoders) and exteroceptive sensors able to provide relative observations between them. In these observations, one robot detects and identifies the other one an ..."
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Cited by 25 (3 self)
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Abstract — In this paper the problem of localize two mobile robots is considered. The robots are equipped with proprioceptive sensors (like encoders) and exteroceptive sensors able to provide relative observations between them. In these observations, one robot detects and identifies the other one and measures some relative quantity. An observability analysis is performed by taking into account the system nonlinearities and for four different relative observations. The theoretical results are validated by simulations and experiments carried out on real platforms. In these experiments, an Extended Kalman Filter is adopted to fuse the information coming from the encoders and the sensors performing the observations. I.
