Unscented SLAM for large-scale outdoor environments

Martínez-Cantín, Rubén; Castellanos, José A.

doi:10.1109/iros.2005.1545002

Cited by 96 publications

(67 citation statements)

References 16 publications

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“…In recent years, research on the simultaneous localization and mapping (SLAM) problem has been brought from indoor applications to large-scale outdoor scenes [18], which makes them interesting for driver assistance applications. While originally SLAM methods based on range measurements from a laser range finder, recent work has also focused on developing camera-based approaches to SLAM [7][15] [22].…”

Section: A Related Workmentioning

confidence: 99%

Visual features for vehicle localization and ego-motion estimation

Pink

Moosmann

Bachmann

2009

2009 IEEE Intelligent Vehicles Symposium

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Abstract-This paper introduces a novel method for vehicle pose estimation and motion tracking using visual features. The method combines ideas from research on visual odometry with a feature map that is automatically generated from aerial images into a Visual Navigation System. Given an initial pose estimate, e.g. from a GPS receiver, the system is capable of robustly tracking the vehicle pose in geographical coordinates over time, using image data as the only input.Experiments on real image data have shown that the precision of the position estimate with respect to the feature map typically lies within only several centimeters. This makes the algorithm interesting for a wide range of applications like navigation, path planning or lane keeping.

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Section: A Related Workmentioning

confidence: 99%

Visual features for vehicle localization and ego-motion estimation

Pink

Moosmann

Bachmann

2009

2009 IEEE Intelligent Vehicles Symposium

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“…RELATED WORK An Extended Kalman Filter based SLAM Solution (EKF-SLAM) was first introduced in a seminal paper by Smith et al [5]. Since then, many contributed using algorithms based on Monte Carlo Sampling (FastSLAM) [6] & Unscented Kalman Filter based approach (UKF-SLAM) [7], etc. [9].…”

Section: Introductionmentioning

confidence: 99%

An experimental distributed framework for distributed Simultaneous Localization and Mapping

Gamage

Tüceryan

2016

2016 IEEE International Conference on Electro Information Technology (EIT)

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Simultaneous Localization and Mapping (SLAM) is widely used in applications such as rescue, navigation, semantic mapping, augmented reality and home entertainment applications. Most of these applications would do better if multiple devices are used in a distributed setting. The distributed SLAM research would benefit if there is a framework where the complexities of network communication is already handled. In this paper we introduce such framework utilizing open source Robot Operating System (ROS) and VirtualBox virtualization software. Furthermore, we describe a way to measure communication statistics of the distributed SLAM system.

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“…In the last few years, some works have been reported which propose either alternative linearization techniques [6,7] or even nonparametric approaches [8,9] to avoid those difficulties.…”

Section: Introductionmentioning

confidence: 99%

Robocentric map joining: Improving the consistency of EKF-SLAM

Castellanos

Martínez-Cantín

Tardós

et al. 2007

Robotics and Autonomous Systems

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190

141

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In this paper 1 we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKF-SLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the EKF-SLAM out of consistency, specially in those situations where uncertainty surpasses a certain threshold. We propose a mapping algorithm, Robocentric Map Joining, which improves consistency of the EKF-SLAM algorithm by limiting the level of uncertainty in the continuous evolution of the stochastic map: (1) by building a sequence of independent local maps, and (2) by using a robot centered representation of each local map. Simulations and a large-scale indoor/outdoor experiment validate the proposed approach.

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Unscented SLAM for large-scale outdoor environments

Cited by 96 publications

References 16 publications

Visual features for vehicle localization and ego-motion estimation

Visual features for vehicle localization and ego-motion estimation

An experimental distributed framework for distributed Simultaneous Localization and Mapping

Robocentric map joining: Improving the consistency of EKF-SLAM

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