54 research outputs found
Multi-Robot FastSLAM for Large Domains
For a robot to build a map of its surrounding area, it must have accurate position information within the area, and to obtain accurate position information within the area, the robot needs to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these maps poses a serious limitation to the algorithm\u27s capability when the problem space is large. The problem will only get worse if the algorithm is adapted to multi-robot mapping. This thesis presents an alternate mapping algorithm that extends the single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations (ASR) to represent the world. But each particle still maintains a local grid to map its vicinity and periodically this grid map is converted into an ASR. An ASR expresses a world in polygons requiring only a minimal amount of memory space. By using this altered mapping strategy, the problem faced in FastSLAM when mapping a large domain can be alleviated. In this algorithm, each robot maps separately, and when two robots encounter each other they exchange range and odometry readings from their last encounter to this encounter. Each robot then sets up another filter for the other robot\u27s data and incrementally updates its own map, incorporating the passed data and its own data at the same time. The passed data is processed in reverse by the receiving robot as if a virtual robot is back-tracking the path of the other robot. The algorithm is demonstrated using three data sets collected using a single robot equipped with odometry and laser-range finder sensors
CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey
Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized
Localização e mapeamento eficiente para robótica : algoritmos e ferramentas
Doutoramento conjunto em InformáticaUm dos problemas fundamentais em robótica é a capacidade de estimar
a pose de um robô móvel relativamente ao seu ambiente. Este problema é
conhecido como localização robótica e a sua exatidão e eficiência têm um impacto
direto em todos os sistemas que dependem da localização. Nesta tese,
abordamos o problema da localização propondo um algoritmo baseado em
scan matching com otimização robusta de mínimos quadrados não lineares
em manifold com a utilização de um campo de verosimilhança contínuo
como modelo de perceção. Esta solução oferece uma melhoria percetível na
eficiência computacional sem perda de exatidão.
Associado à localização está o problema de criar uma representação geométrica
(ou mapa) do meio ambiente recorrendo às medidas disponíveis,
um problema conhecido como mapeamento. No mapeamento a representação
geométrica mais popular é a grelha volumétrica que discretiza o espaço
em volumes cúbicos de igual tamanho. A implementação direta de
uma grelha volumétrica oferece acesso direto e rápido aos dados mas requer
uma quantidade substancial de memória. Portanto, propõe-se uma estrutura
de dados híbrida, com divisão esparsa do espaço combinada com uma
subdivisão densa do espaço que oferece tempos de acesso eficientes com alocações
de memória reduzidas. Além disso, também oferece um mecanismo
integrado de compressão de dados para reduzir ainda mais o uso de memória
e uma estrutura de partilha de dados implícita que duplica dados, de forma
eficiente, quando necessário recorrendo a uma estratégia copy-on-write. A
implementação da solução descrita é disponibilizada na forma de uma biblioteca
de software que oferece um framework para a criação de modelos
baseados em grelhas volumétricas, e.g. grelhas de ocupação. Como existe
uma separação entre o modelo e a gestão de espaço, todas as funcionalidades
da abordagem esparsa-densa estão disponíveis para qualquer modelo
implementado com o framework.
O processo de mapeamento é um problema complexo considerando que localização
e mapeamento são resolvidos simultaneamente. Este problema, conhecido
como localização e mapeamento simultâneo (SLAM), tem tendência
a de consumir recursos consideráveis à medida que a exigência na qualidade
do mapeamento aumenta. De modo a contribuir para o aumento da eficiência,
esta tese apresenta duas solução de SLAM. Na primeira abordagem, o
algoritmo de localização é adaptado ao mapeamento incremental que, em
combinação com o framework esparso-denso, oferece uma solução de SLAM
online computacionalmente eficiente. O resultados obtidos são comparados
com outras soluções disponíveis na literatura recorrendo a um benchmark de
SLAM. Os resultados obtidos demonstram que a nossa solução oferece uma
boa eficiência sem comprometer a exatidão. A segunda abordagem combina
o nosso SLAM online com um filtro de partículas Rao-Blackwellized
para propor uma solução de full SLAM com um grau elevado de eficiência
computacional. A solução inclui propostas de distribuição melhorada com refinamento
de pose através de scan matching, re-amostragem adaptativa com
pesos de amostragem suavizados, partilha eficiente de dados entre partículas
da mesma geração e suporte para multi-threading.One of the most basic perception problems in robotics is the ability to estimate
the pose of a mobile robot relative to the environment. This problem
is known as mobile robot localization and its accuracy and efficiency has a
direct impact in all systems than depend on localization. In this thesis, we
address the localization problem by proposing an algorithm based on scan
matching with robust non-linear least squares optimization on a manifold
that relies on a continuous likelihood field as measurement model. This solution
offers a noticeable improvement in computational efficiency without
losing accuracy.
Associated with localization is the problem of creating the geometric representation
(or map) of the environment using the available measurements, a
problem known as mapping. In mapping, the most popular geometric representation
is the volumetric grid that quantizes space into cubic volumes
of equal size. The regular volumetric grid implementation offers direct and
fast access to data but requires a substantial amount of allocated memory.
Therefore, in this thesis, we propose a hybrid data structure with sparse division
of space combined with dense subdivision of space that offers efficient
access times with reduced memory allocation. Additionally, it offers an online
data compression mechanism to further reduce memory usage and an implicit
data sharing structure that efficiently duplicates data when needed using a
thread safe copy-on-write strategy. The implementation of the solution is
available as a software library that provides a framework to create models
based on volumetric grids, e.g. occupancy grids. The separation between
the model and space management makes all features of the sparse-dense
approach available to every model implemented with the framework.
The process of mapping is a complex problem, considering that localization
and mapping have to be solved simultaneously. This problem, known as
simultaneous localization and mapping (SLAM), has the tendency to consume
considerable resources as the mapping quality requirements increase.
As an effort to increase the efficiency of SLAM, this thesis presents two
SLAM solutions. The first proposal adapts our localization algorithm to incremental
mapping that, in combination with the sparse-dense framework,
provides a computationally efficient online SLAM solution. Using a SLAM
benchmark, the obtained results are compared with other solutions found
in the literature. The comparison shows that our solution provides good
efficiency without compromising accuracy. The second approach combines
our online SLAM with a Rao-Blackwellized particle filter to propose a highly
computationally efficient full SLAM solution. It includes an improved proposal
distribution with scan matching pose refinement, adaptive resampling
with smoothed importance weight, efficient sharing of data between sibling
particles and multithreading support
On Consistent Mapping in Distributed Environments using Mobile Sensors
The problem of robotic mapping, also known as simultaneous localization and mapping (SLAM), by a mobile agent for large distributed environments is addressed in this dissertation. This has sometimes been referred to as the holy grail in the robotics community, and is the stepping stone towards making a robot completely autonomous. A hybrid solution to the SLAM problem is proposed based on "first localize then map" principle. It is provably consistent and has great potential for real time application. It provides significant improvements over state-of-the-art Bayesian approaches by reducing the computational complexity of the SLAM problem without sacrificing consistency. The localization is achieved using a feature based extended Kalman filter (EKF) which utilizes a sparse set of reliable features. The common issues of data association, loop closure and computational cost of EKF based methods are kept tractable owing to the sparsity of the feature set. A novel frequentist mapping technique is proposed for estimating the dense part of the environment using the sensor observations. Given the pose estimate of the robot, this technique can consistently map the surrounding environment. The technique has linear time complexity in map components and for the case of bounded sensor noise, it is shown that the frequentist mapping technique has constant time complexity which makes it capable of estimating large distributed environments in real time. The frequentist mapping technique is a stochastic approximation algorithm and is shown to converge to the true map probabilities almost surely. The Hybrid SLAM software is developed in the C-language and is capable of handling real experimental data as well as simulations. The Hybrid SLAM technique is shown to perform well in simulations, experiments with an iRobot Create, and on standard datasets from the Robotics Data Set Repository, known as Radish. It is demonstrated that the Hybrid SLAM technique can successfully map large complex data sets in an order of magnitude less time than the time taken by the robot to acquire the data. It has low system requirements and has the potential to run on-board a robot to estimate large distributed environments in real time
RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously
navigate through, identify, and reach areas of interest; and there recognize,
localize, and manipulate work tools to perform complex manipulation tasks. The
proposed contribution includes a modular software architecture where each
module solves specific sub-tasks and that can be easily enlarged to satisfy new
requirements. Included indoor and outdoor tests demonstrate the capability of
the proposed system to autonomously detect a target object (a panel) and
precisely dock in front of it while avoiding obstacles. They show it can
autonomously recognize and manipulate target work tools (i.e., wrenches and
valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve
stem). A specific case study is described where the proposed modular
architecture lets easy switch to a semi-teleoperated mode. The paper
exhaustively describes description of both the hardware and software setup of
RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International
Robotics Challenge, and the lessons we learned when participating at this
competition, where we ranked third in the Gran Challenge in collaboration with
the Czech Technical University in Prague, the University of Pennsylvania, and
the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics,
published by Taylor & Franci
Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping
International audienceNowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment
Online Mapping and Perception Algorithms for Multi-robot Teams Operating in Urban Environments.
This thesis investigates some of the sensing and perception challenges faced
by multi-robot teams equipped with LIDAR and camera
sensors. Multi-robot teams are ideal for deployment in large,
real-world environments due to their ability to parallelize exploration,
reconnaissance or mapping tasks.
However, such domains also impose additional requirements, including the
need for a) online algorithms (to eliminate stopping and waiting for
processing to finish before proceeding) and b) scalability (to handle
data from many robots distributed over a large area).
These general requirements give rise to specific algorithmic challenges, including 1) online maintenance of large, coherent
maps covering the explored area, 2) online estimation of communication properties
in the presence of buildings and other interfering structure, and 3)
online fusion and segmentation of multiple sensors to aid in object detection.
The contribution of this thesis is the introduction of novel
approaches that leverage grid-maps and sparse multi-variate gaussian
inference to augment the capability of multi-robot teams operating in
urban, indoor-outdoor environments by improving the state of the art
of map rasterization, signal strength prediction, colored point cloud
segmentation, and reliable camera calibration.
In particular, we introduce a map rasterization technique for large
LIDAR-based occupancy grids that makes online updates possible when
data is arriving from many robots at once. We also introduce new
online techniques for robots to predict the signal strength to their
teammates by combining LIDAR measurements with signal strength
measurements from their radios. Processing fused LIDAR+camera point
clouds is also important for many object-detection pipelines. We
demonstrate a near linear-time online segmentation algorithm to this
domain. However, maintaining the calibration of a fleet of 14 robots
made this approach difficult to employ in practice.
Therefore we introduced a robust and repeatable
camera calibration process that grounds the camera model uncertainty in pixel
error, allowing the system to guide novices and experts alike to reliably produce accurate calibrations.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113516/1/jhstrom_1.pd
Improved Particle Filter Based Localization and Mapping Techniques
One of the most fundamental problems in mobile robotics is localization. The solution to most problems requires that the robot first determine its location in the environment. Even if the absolute position is not necessary, the robot must know where it is in relation to other objects. Virtually all activities require this preliminary knowledge. Another part of the localization problem is mapping, the robot’s position depends on its representation of the environment. An object’s position cannot be known in isolation, but must be determined in relation to the other objects. A map gives the robot’s understanding of the world around it, allowing localization to provide a position within that representation. The quality of localization thus depends directly on the quality of mapping. When a robot is moving in an unknown environment these problems must be solved simultaneously in a problem called SLAM (Simultaneous Localization and Mapping). Some of the best current techniques for localization and SLAM are based on particle filters which approximate the belief state. Monte Carlo Localization (MCL) is a solution to basic localization, while FastSLAM is used to solve the SLAM problem. Although these techniques are powerful, certain assumptions reduce their effectiveness. In particular, both techniques assume an underlying static environment, as well as certain basic sensor models. Also, MCL applies to the case where the map is entirely known while FastSLAM solves an entirely unknown map. In the case of partial knowledge, MCL cannot succeed while FastSLAM must discard the additional information. My research provides improvements to particle based localization and mapping which overcome some of the problems with these techniques, without reducing the original capabilities of the algorithms. I also extend their application to additional situations and make them more robust to several types of error. The improved solutions allow more accurate localization to be performed, so that robots can be used in additional situations
Characterisation of a nuclear cave environment utilising an autonomous swarm of heterogeneous robots
As nuclear facilities come to the end of their operational lifetime, safe decommissioning becomes a more prevalent issue. In many such facilities there exist ‘nuclear caves’. These caves constitute areas that may have been entered infrequently, or even not at all, since the construction of the facility. Due to this, the topography and nature of the contents of these nuclear caves may be unknown in a number of critical aspects, such as the location of dangerous substances or significant physical blockages to movement around the cave. In order to aid safe decommissioning, autonomous robotic systems capable of characterising nuclear cave environments are desired. The research put forward in this thesis seeks to answer the question: is it possible to utilise a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment? This is achieved through examination of the three key components comprising a heterogeneous swarm: sensing, locomotion and control. It will be shown that a heterogeneous swarm is not only capable of performing this task, it is preferable to a homogeneous swarm. This is due to the increased sensory and locomotive capabilities, coupled with more efficient explorational prowess when compared to a homogeneous swarm
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