A new sensor model for particle-filter based localization in the partially unknown environments

Yilmaz, Sezcan; Kayir, Hilal Ezercan; Kaleci, Burak; Parlaktuna, Osman

doi:10.1109/icsmc.2010.5641781

Cited by 1 publication

(2 citation statements)

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“…The model is based on the idea of tracking the ground area inside the free space (not occupied cells) of a known map. To reduce the effects of outliers (Yilmaz et al, 2010) calculates the total sensor probability through replacing the process of multipling individual sensor probabilities by individual probabilities geometric mean after repealing some extreme measurements. In this research an improvement in calculating the likelihood function is proposed through neglecting the measurements that suspected to invalidate the independent noise assumption.…”

Section: Related Workmentioning

confidence: 99%

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An Improvement in the Observation Model for Monte Carlo Localization

Alhashimi

Hostettler

Gustafsson

2014

Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics

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Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.

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Section: Related Workmentioning

confidence: 99%

“…In each iteration of MCL, the so-called observation model is used to correct the proposal distribution. One of the key challenges in context of probabilistic localization, however, lies in the design of the observation model (Pfaff et al, 2006;Pfaff et al, 2007;Pfaff et al, 2008b;Olufs and Vincze, 2009;Yilmaz et al, 2010).…”

Section: Introductionmentioning

confidence: 99%