Mapping Based on a Noisy Range-Only Sensor

Herranz, Fernando Miguel Pérez; Ocaña, Manuel; Bergasa, Luis M.; Hern, N.; ndez,; Llamazares, Ángel; Fern, C.

doi:10.1007/978-3-642-27579-1_54

Cited by 3 publications

(7 citation statements)

References 8 publications

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“…The RF mapping process that we propose is based on our previous work [26]. It makes possible to estimate the position of the Waspmotes using the distance between them and the vehicle.…”

Section: F Rf Mappingmentioning

confidence: 99%

Robust traffic signs detection by means of vision and V2I communications

García-Garrido

Ocaña

Llorca

et al. 2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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This paper presents a complete traffic sign recognition system, including the steps of detection, recognition and tracking. The Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM), and is able to recognize up to one hundred of the main road signs. Besides a novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed, for that purpose vehicle-to-infrastructure (V2I) communication and stereo information is used. This paper presents plenty of tests in real driving conditions, both day and night, in which a high success rate and low number of false negatives and true positives were obtained, and an average runtime of 35 ms, allowing real-time performance.

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“…The RF mapping process that we propose is based on our previous work [26]. It makes possible to estimate the position of the Waspmotes using the distance between them and the vehicle.…”

Section: F Rf Mappingmentioning

confidence: 99%

Robust traffic signs detection by means of vision and V2I communications

García-Garrido

Ocaña

Llorca

et al. 2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…The particle filter is described in our previous work. 19 The beacon's location is added to the EKF when the particle filter has converged. The convergence is determined by a combination of two analyses: first, it is verified that the particle filter is only following one hypothesis by means of clustering; second, the Mardia multivariate normality test using skewness and kurtosis 20 is used to determine if the null hypothesis of multivariate normality is a reasonable assumption regarding the particle population.…”

Section: Algorithm 1 Ekf Slam With Ro Sensorsmentioning

confidence: 99%

“…4. Update step (lines [11][12][13][14][15][16][17][18][19][20][21][22]. The FastSLAM updates the posterior over the beacon feature estimates which are represented by the particle filter estimation.…”

Section: Algorithm 2 Fastslam Algorithm With Ro Sensorsmentioning

confidence: 99%

“…If the motion model or the measurement model are non-linear, non-linear optimization techniques such as Gauss-Newton optimization or Levenberg-Marquardt algorithm can solve the linear approximations to Eq. (19) in order to approach the minimum. * = arg min…”

Section: Sam Sammentioning

confidence: 99%

“…3. Optimization (lines [17][18][19][20]. Once the factor graph has been built and the initial estimation has been set up, the SLAM problem can be solved as shown in Eq.…”

Section: Algorithm 3 Sam Algorithm With Ro Sensorsmentioning

confidence: 99%

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WiFi SLAM algorithms: an experimental comparison

et al. 2014

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SUMMARYLocalization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.

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Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

García-Garrido

Ocaña

Llorca

et al. 2012

Sensors

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This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

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Mapping Based on a Noisy Range-Only Sensor

Cited by 3 publications

References 8 publications

Robust traffic signs detection by means of vision and V2I communications

Robust traffic signs detection by means of vision and V2I communications

WiFi SLAM algorithms: an experimental comparison

Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

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