Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation

Núñez, Pedro; Vázquez-Martín, Ricardo; Toro, J.C. del; Bandera, Antonio; Hernández, Francisco

doi:10.1016/j.robot.2007.07.005

Cited by 46 publications

(44 citation statements)

References 31 publications

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“…The range samples are spaced every half a degree, all within the same plane. The set of natural features used in the EKF-SLAM algorithm, and subsequently in the submap generation process, has been extracted using a curvature-based algorithm for laser scan data segmentation [17]. The whole segmentation process consists of two stages.…”

Section: Resultsmentioning

confidence: 99%

“…As it is shown in Figure 5, the same local maps are created even when the robot follows different trajectories through the same environment. In this Figure the information included in the CG is based on feature maps, being these features obtained using a curvature-based environment description from laser scan data [17]. However, the next section shows how this algorithm is able to be used for any sort of features and sensor.…”

Section: Validation Of the Graph Partitionmentioning

confidence: 99%

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Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework

Vázquez-Martín

Núñez

Bandera

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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Hybrid maps combine metric and topological information for efficiently managing large-scale environments. In a feature-based mapping framework, this paper describes the application of a spectral clustering approach for automatically detecting the transitions between subsequently traversed local maps. Contrary to recently proposed approaches, this algorithm considers each individual map feature as a node of a graph whose edges link two nodes if they are simultaneously observed. Thus, given a sequence of observations, an auxiliary graph is incrementally built whose edges carry non-negative weights according to the locality of the features. Given a feature, its locality defines the set of features that has been observed simultaneously with it at least once. At each execution of the mapping approach, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. If the graph partition is validated, the algorithm determines that the robot is moving into a new area and a new local map is generated. We have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision. Experimental results demonstrate the performance of the proposal.

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Section: Resultsmentioning

confidence: 99%

Section: Validation Of the Graph Partitionmentioning

confidence: 99%

Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework

Vázquez-Martín

Núñez

Bandera

et al. 2009

2009 IEEE International Conference on Robotics and Automation

Self Cite

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“…In [10,21], a median filter is used to filter out some of the outliers and noises, while a six order polynomial fitting is used in [22,23] between the measured distance and the true distance to compensate for the systematic error.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

et al. 2015

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Abstract:The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle's integrated navigation accuracy. OPEN ACCESSMicromachines 2015, 6 197

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“…Training classifier with samples(learning) followed by recognition of scenes using trained classifier achieved a recognition phase [1][2][3][4][5][6][7][8]. Yet there are limitations and disadvantages with this recognition architecture, which is, (i) since it is off-line and batch learning for classifier, the classifier cannot be updated when it comes to new sample unless learning repeatedly with previous learned samples in an off-line mode; (ii) the adaptability and robustness of the recognition system is bad in new situations since it is unable to detect novel scenes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Subspace Based Online PCA Algorithm for Mobile Robot Scene Learning and Recognition

Yao

et al. 2011

2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics

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The learning method for visual scene recognition that compute a space of eigenvectors by Principal Component Analysis(PCA) traditionally require a batch computation step, in which the only way to update the subspace is to rebuild the subspace by the scratch when it comes to new samples. In this paper, we introduce a new approach to scene recognition based on online PCA algorithm with adaptive subspace, which allows for complete incremental learning. We propose to use different subspace updating strategy for new sample according to the degree of difference between new sample and learned sample, which can improve the adaptability in different situations, and also reduce the time of calculation and storage space. The experimental results show that the proposed method can recognize the unknown scene, realizing online scene accumulation and updating, and improving the recognition performance of system.

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Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation

Cited by 46 publications

References 31 publications

Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework

Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

Adaptive Subspace Based Online PCA Algorithm for Mobile Robot Scene Learning and Recognition

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