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36
Augmenting inertial navigation with image-based motion estimation
- in IEEE International Conference on Robotics and Automation, Washington D.C., 2002
, 2002
"... Numerous upcoming NASA misions need to land safely and precisely on planetary bodies. Accurate and robust state estimation during the descent phase is necessary. Towards this end, we have developed a new approach for improved state estimation by augmenting traditional inertial navigation techniques ..."
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Cited by 87 (14 self)
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Numerous upcoming NASA misions need to land safely and precisely on planetary bodies. Accurate and robust state estimation during the descent phase is necessary. Towards this end, we have developed a new approach for improved state estimation by augmenting traditional inertial navigation techniques with image-based motion estimation (IBME). A Kalman filter that processes rotational velocity and linear acceleration measurements provided from an IMU has been enhanced to accomodate relative pose measurements from the IBME. In addition to increased state estimation accuracy, IBME convergence time is reduced while robustness of the overall approach is improved. The methodology is described in detail and experimental results with a 5DOF gantry testbed are 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.
LOST: Localization-Space Trails for Robot Teams
- IEEE Transactions on Robotics and Automation
, 2002
"... We describe Localization-Space Trails (LOST), a method that enables a team of robots to navigate between places of interest in an initially unknown environment using a trail of landmarks. The landmarks are not physical; they are waypoint coordinates generated on-line by each robot and shared with te ..."
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Cited by 51 (13 self)
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We describe Localization-Space Trails (LOST), a method that enables a team of robots to navigate between places of interest in an initially unknown environment using a trail of landmarks. The landmarks are not physical; they are waypoint coordinates generated on-line by each robot and shared with team-mates. Waypoints are specified in each robot's local coordinate system, and contain references to features in the world that are relevant to the team's task and common to all robots. Using these task-level references, robots can share waypoints without maintaining a global coordinate system.
Bayesian estimation and Kalman filtering: A unified framework for Mobile Robot Localization
, 2000
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Robust Localization Using Relative and Absolute Position Estimates
, 1999
"... A low cost strategy based on well calibrated odometry is presented for localizing mobile robots. The paper describes a two-step process for correction of 'systematic errors' in encoder measurements followed by fusion of the calibrated odometry with a gyroscope and GPS resulting in a robust ..."
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Cited by 37 (6 self)
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A low cost strategy based on well calibrated odometry is presented for localizing mobile robots. The paper describes a two-step process for correction of 'systematic errors' in encoder measurements followed by fusion of the calibrated odometry with a gyroscope and GPS resulting in a robust localization scheme. A Kalman filter operating on data from the sensors is used for estimating position and orientation of the robot. Experimental results are presented that show an improvement of at least one order of magnitude in accuracy compared to the un-calibrated, un-filtered case. Our method is systematic, simple and yields very good results. We show that this strategy proves useful when the robot is using GPS to localize itself as well as when GPS becomes unavailable for some time. As a result robot can move in and out of enclosed spaces, such as buildings, while keeping track of its position on the fly.
Vision guided landing of an autonomous helicopter in hazardous terrain
- in Proceedings of IEEE Internation Conference of Robotics and Automation
, 2005
"... Abstract – Future robotic space missions will employ a precision soft-landing capability that will enable exploration of previously inaccessible sites that have strong scientific significance. To enable this capability, a fully autonomous onboard system that identifies and avoids hazardous features ..."
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Cited by 30 (2 self)
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Abstract – Future robotic space missions will employ a precision soft-landing capability that will enable exploration of previously inaccessible sites that have strong scientific significance. To enable this capability, a fully autonomous onboard system that identifies and avoids hazardous features such as steep slopes and large rocks is required. Such a system will also provide greater functionality in unstructured terrain to unmanned aerial vehicles. This paper describes an algorithm for landing hazard avoidance based on images from a single moving camera. The core of the algorithm is an efficient application of structure from motion to generate a dense elevation map of the landing area. Hazards are then detected in this map and a safe landing site is selected. The algorithm has been implemented on an autonomous helicopter testbed and demonstrated four times resulting in the first autonomous landing of an unmanned helicopter in unknown and hazardous terrain. Index Terms – autonomous landing, hazard detection, structure from motion, UAV. 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.
Smoother based 3-D Attitude Estimation for Mobile Robot Localization
- Proc. 1999 IEEE International Conference on Robotics and Automation
, 1999
"... The mobile robot localization problem is decomposed into two stages � attitude estimation followed byposition estimation. The innovation of our method is the use of a smoother, in the attitude estimation loop that outperforms other Kalman ter based techniques in estimate accuracy. The smoother explo ..."
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Cited by 19 (8 self)
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The mobile robot localization problem is decomposed into two stages � attitude estimation followed byposition estimation. The innovation of our method is the use of a smoother, in the attitude estimation loop that outperforms other Kalman ter based techniques in estimate accuracy. The smoother exploits the special nature of the data fused � high frequency inertial sensor (gyroscope) data and low frequency absolute orientation data (from acompass or sun sensor). Two Kalman lters form the smoother. During each time interval one of them propagates the attitude estimate forward in time until it is updated by an absolute orientation sensor. At this time, the second lter propagates the recently renewed estimate back in time. The smoother optimally exploits the limited observability of the system by combining the outcome of the two lters. The system model uses gyro modeling which relies on integrating the kinematic equations to propagate the attitude estimates and obviates the need for complex dynamic modeling. The Indirect (error state) form of the Kalman lter is developed forboth parts of the smoother. The proposed approach is independent of the robot structure and the morphology of the ground. It can easily be transfered to another robot which has an equivalent set of sensors. Quaternions are used for the 3D attitude representation, mainly for practical reasons discussed in the paper. The proposed innovative algorithm is tested in simulation and the overall improvement in position estimation is demonstrated. 1
Slip-compensated path following for planetary exploration rovers
- Advanced Robotics
, 2006
"... A system that enables continuous slip compensation for a Mars rover has been designed, imple-mented, and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage, and reach intended goals in high ..."
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Cited by 18 (4 self)
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A system that enables continuous slip compensation for a Mars rover has been designed, imple-mented, and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage, and reach intended goals in high-slip environments. These components include: visual odometry, vehicle kinematics, a Kalman filter pose estimator, and a slip-compensated path follower. Visual odometry tracks dis-tinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The kinematics for a rocker-bogie suspension system estimates vehicle motion by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter processes mea-surements from an Inertial Measurement Unit (IMU) and visual odometry. The filter estimate is then compared to the kinematic estimate to determine whether slippage has occurred, taking into account estimate uncertainties. If slippage is detected, the slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.
Applications of self-organising multi-agent systems: an initial framework of comparison. Informatica
, 2005
"... A lot of work is devoted to formalizing and devising architectures for agents ' cooperative behaviour, for coordinating the behaviour of individual agents within groups, as well as to designing agent societies using social laws. However, providing agents with abilities to automatically devise s ..."
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Cited by 13 (0 self)
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A lot of work is devoted to formalizing and devising architectures for agents ' cooperative behaviour, for coordinating the behaviour of individual agents within groups, as well as to designing agent societies using social laws. However, providing agents with abilities to automatically devise societies so as to form coherent emergent groups that coordinate their behaviour via social laws, is highly challenging. These systems are called self-organised. We are beginning to understand some of the ways in which selforganised agent systems can be devised. In this perspective, this paper provides several examples of multi-agent systems in which self-organisation, based on different mechanisms, is used to solve complex problems. Several criteria for comparison of self-organisation between the different applications are provided. Povzetek: Članek opisuje primere in kriterije samoorgarnizacije v agentnih sistemih. 1
