Efficient Multi-hypotheses Unscented Kalman Filtering for Robust Localization

Jochmann, Gregor; Kerner, Sören; Tasse, Stefan; Urbann, Oliver

doi:10.1007/978-3-642-32060-6_19

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…In [40], the authors use a set of Gaussians to model the likelihood function of the robot's location given the information provided by a laser range finder. In [41], the authors combine Kalman and particle filters.…”

Section: Related Workmentioning

confidence: 99%

Differential Evolution Markov Chain Filter for Global Localization

Moreno

Alonso-Martín

Muñoz

et al. 2015

J Intell Robot Syst

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A key challenge for an autonomous mobile robot is to estimate its location according to the avail able information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evo lution method that solves this problem in 2D and 3D environments. The robot's pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Dif ferential Evolution method to solve efficiently many optimization problems. In this work, we have com bined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.

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Section: Related Workmentioning

confidence: 99%

Differential Evolution Markov Chain Filter for Global Localization

Moreno

Alonso-Martín

Muñoz

et al. 2015

J Intell Robot Syst

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“…The robot localization and tracking problem has been studied in the RoboCup domain, and different filtering techniques have been discussed, such as the multiple model Kalman filter by Quinlan et al [27] and the multi-hypotheses unscented Kalman filtering (UKF) by Jochmann et al [28]. In [29], Team B-Human proposed to combine the particle filter with UKF for self localization, and EKF is used for ball tracking.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Moving Objects: Multi-Robot Simultaneous Localization and Tracking

Chang

Wang

2016

IEEE Trans. Automat. Sci. Eng.

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Cooperative localization has been proved to effectively outperform single-robot localization. While most of the state-of-the-art multi-robot localization systems either treat moving objects as outliers or accomplish moving object tracking separately from localization, we argue that augmenting moving objects into the localization estimation can further enhance localization performance and is indeed the key to solve several localization challenges such as insufficient map features, no map features, and symmetric maps. In this paper, a multi-robot simultaneous localization and tracking (MR-SLAT) algorithm based on the extended Kalman filter is proposed, and multiple hypothesis tracking (MHT) is integrated into MR-SLAT for dealing with challenging data association issues. The proposed approach is verified in two scenarios: the NAO humanoid robots equipped with cameras and WiFi are used in the RoboCup scenario and the robotic vehicles with laser scanners and dedicated short-range communications (DSRC) are used in the traffic scenario. The experiments with ground truth show that MR-SLAT, by exploiting moving objects, is superior to single-robot localization and cooperative localization in challenging scenarios. Ample experimental and simulation results demonstrate the effectiveness of exploiting moving objects and the generality and feasibility of the proposed MR-SLAT algorithm.Note to Practitioners: Abstract-To apply robot systems in realworld dynamic scenes, the capability of dealing with moving objects is necessary and critical. The key insight of this work is that by exploiting moving objects, many practical localization cases that are challenging for approaches which filter out or separately deal with moving objects can be solved. The main observation is that in the multi-robot scenario, the commonly observed moving objects actually contain valuable information that helps localization. The proposed MR-SLAT algorithm exploits the mutual benefits between localization and tracking by maintaining the correlations between teammate robots and moving objects through the augmented state and thus further improves localization accuracy and reliability. In addition, in the proposed system, data association, which is one of the most critical parts for robot systems to work successfully in practical challenging scenes, is tackled by integrating MHT, and the feasibility is experimentally demonstrated and analyzed. The applicability of the proposed system and the concept of exploiting moving objects are verified through ample experiments in two different scenarios with different platforms and sensors. The manuscript, along with the proposed MR-SLAT approach, accordingly makes contributions to practical multi-agent applications such as intelligent transportation systems.Index Terms-Cooperative systems, multi-sensor systems, robot sensing systems.

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“…Having a good world model like this enables the robot to make smart strategic decisions during the game. Like many other teams at the competition [7,4], since 2011 we have used a multi-modal 7-state Unscented Kalman Filter (UKF) for localization [2]. The UKF provides a number of benefits compared to other approaches such as Monte Carlo localization as it is computationally efficient and enables easy sharing and integration of ball information between teammates.…”

Section: Localizationmentioning

confidence: 99%

The 2012 UT Austin Villa Code Release

Barrett

Genter

et al. 2014

RoboCup 2013: Robot World Cup XVII

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Abstract. In 2012, UT Austin Villa claimed the Standard Platform League championships at both the US Open and the 2012 RoboCup competition held in Mexico City. This paper describes the code release associated with the team and discusses the key contributions of the release. This release will enable teams entering the Standard Platform League and researchers using the Naos to have a solid foundation from which to start their work as well as providing useful modules to existing researchers and RoboCup teams. We expect it to be of particular interest because it includes the architecture, logic modules, and debugging tools that led to the team's success in 2012. This architecture is designed to be flexible and robust while enabling easy testing and debugging of code. The vision code was designed for easy use in creating color tables and debugging problems. A custom localization simulator that is included permits fast testing of full team scenarios. Also included is the kick engine which runs through a number of static joint poses and adapts them to the current location of the ball. This code release will provide a solid foundation for new RoboCup teams and for researchers that use the Naos.

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Efficient Multi-hypotheses Unscented Kalman Filtering for Robust Localization

Cited by 17 publications

References 6 publications

Differential Evolution Markov Chain Filter for Global Localization

Differential Evolution Markov Chain Filter for Global Localization

Exploiting Moving Objects: Multi-Robot Simultaneous Localization and Tracking

The 2012 UT Austin Villa Code Release

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