Robust sensor characterization via max-mixture models: GPS sensors

Morton, Ryan; Olson, Edwin

doi:10.1109/iros.2013.6696402

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…To model a more complex cost distribution, we use a mixture of two Gaussians. Gaussian mixtures have prior use in compactly approximating real-world events [33] [34].…”

Section: A Problem Statementmentioning

confidence: 99%

Monte-Carlo Policy-Tree Decision Making

Haggenmiller¹,

Olson²

2022

Preprint

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In this paper, we propose Monte-Carlo Policy-Tree Decision Making (MCPTDM), an uncertainty-aware framework for high-variance planning problems with multiple dynamic agents. Planning when surrounded by multiple uncertain dynamic agents is hard because we cannot be certain of either the initial states or the future actions of those agents, leading to an exponential explosion in possible futures. Many important real-world problems, such as autonomous driving, fit this model. To address these difficulties, we combine Multi-policy Decision Making (MPDM) and Monte Carlo tree search (MCTS) and perform policy tree search with marginal action cost (MAC) estimation and repeated belief particles. We first design a synthetic experiment to evaluate these novel improvements in isolation. Then we evaluate the complete framework in a self-driving car simulation experiment and compare it against MPDM and Efficient Uncertainty-aware Decision Making (EUDM) methods. We release our complete source code for replicating our experiments and results.

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“…To model a more complex cost distribution, we use a mixture of two Gaussians. Gaussian mixtures have prior use in compactly approximating real-world events [33] [34].…”

Section: A Problem Statementmentioning

confidence: 99%

Monte-Carlo Policy-Tree Decision Making

Haggenmiller¹,

Olson²

2022

Preprint

Self Cite

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“…A drawback of both approaches is the lack of concepts to get the required parametrization of the GMM or any other non-Gaussian distribution. Existing approaches [15,16] estimate it in advance which is not possible if the error distribution depends on the environment and varies over time. This paper is build on top of both ideas from Olson and Agarwal [13] / Rosen et al [14], so we provide more details about their concepts in Sec.…”

Section: Prior Workmentioning

confidence: 99%

“…Based on this assumption, we can redefine the observed variable for the GMM estimation problem as (15) and the EM problem of state estimation as (16).…”

Section: Self-tuning Mixturesmentioning

confidence: 99%

Expectation-Maximization for Adaptive Mixture Models in Graph Optimization

Pfeifer

Protzel

2019

2019 International Conference on Robotics and Automation (ICRA)

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Non-Gaussian and multimodal distributions are an important part of many recent robust sensor fusion algorithms. In difference to robust cost functions, they are probabilistically founded and have good convergence properties. Since their robustness depends on a close approximation of the real error distribution, their parametrization is crucial.We propose a novel approach that allows to adapt a multimodal Gaussian mixture model to the error distribution of a sensor fusion problem. By combining expectation-maximization and non-linear least squares optimization, we are able to provide a computationally efficient solution with well-behaved convergence properties.We demonstrate the performance of these algorithms on several real-world GNSS and indoor localization datasets. The proposed adaptive mixture algorithm outperforms state-of-theart approaches with static parametrization. Source code and datasets are available under https://mytuc.org/libRSF.

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“…Some can be cheap, such as infrared and ultrasonic rangefinders [12], RFID [12,7] and GPS [10,17], but others can be quite expensive, like laser rangefinders [13]. They allow robots to acquire information about the environment within certain ranges and depending on certain environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Biologically Inspired Vision for Indoor Robot Navigation

Saleiro

Terzic

Lobato

et al. 2014

Lecture Notes in Computer Science

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Ultrasonic, infrared, laser and other sensors are being applied in robotics. Although combinations of these have allowed robots to navigate, they are only suited for specific scenarios, depending on their limitations. Recent advances in computer vision are turning cameras into useful low-cost sensors that can operate in most types of environments. Cameras enable robots to detect obstacles, recognize objects, obtain visual odometry, detect and recognize people and gestures, among other possibilities. In this paper we present a completely biologically inspired vision system for robot navigation. It comprises stereo vision for obstacle detection, and object recognition for landmark-based navigation. We employ a novel keypoint descriptor which codes responses of cortical complex cells. We also present a biologically inspired saliency component, based on disparity and colour.

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Robust sensor characterization via max-mixture models: GPS sensors

Cited by 8 publications

References 19 publications

Monte-Carlo Policy-Tree Decision Making

Monte-Carlo Policy-Tree Decision Making

Expectation-Maximization for Adaptive Mixture Models in Graph Optimization

Biologically Inspired Vision for Indoor Robot Navigation

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