Simultaneous Localisation and Mapping in Dynamic Environments (SLAMIDE) with Reversible Data Associa

Bibby, Charles; Reid, Ian

doi:10.15607/rss.2007.iii.014

Cited by 57 publications

(49 citation statements)

References 21 publications

(21 reference statements)

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“…In feature based SLAM, coordinates in the global frame are explicitly represented as landmarks, m, which are part of the state vector. The standard assumption is that the landmarks are stationary but dynamic objects can naturally be included in the state vector (Bibby and Reid, 2007). Assume that measurements arrive in the same rate as the dynamic model.…”

Section: Ekf-slammentioning

confidence: 99%

Inertial Navigation and Mapping for Autonomous Vehicles

Skoglund¹

2014

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Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below.We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a socalled simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship.Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined.The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications.We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear least-squares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKF-SLAM.We show how NLS-SLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKF-SLAM. The results obtained using NLS-SLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended RauchTung-Striebel smoother while the second problem is solved with a quasi-Newton method. The results using EM-SLAM are better than NLS-SLAM both in terms of accuracy and complexity.v Populärvetenskaplig sammanfattningVi människor utför dagligen en stor mängd navigerings-och karteringsrutiner utan att ens reflektera över dessa. Allt ifrån att resa mellan hem och arbete till att upptäcka nya spännande miljöer i en ny stad. För navigering i välbekanta miljöer använder vi våra sinnen och den mentala karta av omvärlden som är uppbygd...

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Section: Ekf-slammentioning

confidence: 99%

Inertial Navigation and Mapping for Autonomous Vehicles

Skoglund¹

2014

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“…Alternatively moving objects are detected and tracked separately using multi-target tracking techniques [6]. Another recent approach tries to classify landmarks as moving or stationary, and incorporates reversible data association within a sliding window of recent observations, to allow moving objects to be included into the Simultaneous Localization and Mapping (SLAM) estimate [7]. In general, while these approaches mitigate some problems of classical SLAM algorithms, they cannot handle long-term changes to the structure of an environment.…”

Section: Related Workmentioning

confidence: 99%

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Dayoub

Cielniak

Duckett

2011

Robotics and Autonomous Systems

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Real-world environments such as houses and offices change over time, meaning that a mobile robot's map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metrictopological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.

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“…While a globally consistent solution to the localization problem must necessarily also perform mapping (Thrun, 2003), many applications do not require or benefit from a globally consistent map. Locally consistent approaches such as fixed-time-window SLAM (Bibby and Reid, 2007) and visual odometry (Nistér et al, 2004;Agrawal and Konolige, 2007) have shown great success in applications such as goal-directed navigation and localization in dynamic environments.…”

Section: Fast Rotational Visual Odometrymentioning

confidence: 99%

A multirange architecture for collision‐free off‐road robot navigation

Sermanet

Hadsell

Scoffier

et al. 2008

Journal of Field Robotics

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We present a multi-layered mapping, planning, and command execution system developed and tested on the LAGR mobile robot. Key to robust performance under uncertainty is the combination of a short-range perception system operating at high frame rate and low resolution and a long-range, adaptive vision system operating at lower frame rate and higher resolution. The short-range module performs local planning and obstacle avoidance with fast reaction times, while the long-range module performs strategic visual planning. Probabilistic traversability labels provided by the perception modules are combined and accumulated into a robot-centered hyperbolic-polar map with a 200 meter effective range. Instead of using a dynamical model of the robot for short-range planning, the system uses a large lookup table of physically-possible trajectory segments recorded on the robot in a wide variety of driving conditions. Localization is performed using a combination of GPS, wheel odometry, IMU, and a high-speed, low-complexity rotational visual odometry module. The end to end system was developed and tested on the LAGR mobile robot, and was verified in independent government tests.

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Simultaneous Localisation and Mapping in Dynamic Environments (SLAMIDE) with Reversible Data Associa

Cited by 57 publications

References 21 publications

Inertial Navigation and Mapping for Autonomous Vehicles

Inertial Navigation and Mapping for Autonomous Vehicles

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

A multirange architecture for collision‐free off‐road robot navigation

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