Dependence maximization localization: a novel approach to 2D street-map-based robot localization
TL;DR: This paper proposes a novel localization approach that can be applied to sidewalks based on existing 2D street maps and employs a computationally efficient estimator of squared-loss mutual information, through which it achieves near real-time performance.
Abstract: Recently, localization methods based on detailed maps constructed using simultaneous localization and mapping have been widely used for mobile robot navigation. However, the cost of building such maps increases rapidly with expansion of the target environment. Here, we consider the problem of localization of a mobile robot based on existing 2D street maps. Although a large amount of research on this topic has been reported, the majority of the previous studies have focused on car-like vehicles that navigate on roadways; thus, the efficacy of such methods for sidewalks is not yet known. In this paper, we propose a novel localization approach that can be applied to sidewalks. Whereas roadways are typically marked, e.g. by white lines, sidewalks are not and, therefore, road boundary detection is not straightforward. Thus, obtaining exact correspondence between sensor data and a street map is complex. Our approach to overcoming this difficulty is to maximize the statistical dependence between the sensor data ...
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TL;DR: The design of a virtual angle measurement soft sensing technique based on the information conversion of an optoelectronic signal provided by a rotatory scanning system through an FPGA is described in detail.
16 citations
22 Nov 2017
TL;DR: This method to localize a robot in a global coordinate frame based on a sparse 2D map containing outlines of building and road network information and no location prior information shows considerable performance benefits, as well as the feasibility of global localization based on sparse building outline data.
Abstract: This paper presents a method to localize a robot in a global coordinate frame based on a sparse 2D map containing outlines of building and road network information and no location prior information. Its input is a single 3D laser scan of the surroundings of the robot. The approach extends the generic chamfer matching template matching technique from image processing by including visibility analysis in the cost function. Thus, the observed building planes are matched to the expected view of the corresponding map section instead of to the entire map, which makes a more accurate matching possible. Since this formulation operates on generic edge maps from visual sensors, the matching formulation can be expected to generalize to other input data, e.g., from monocular or stereo cameras. The method is evaluated on two large datasets collected in different real-world urban settings and compared to a baseline method from literature and to the standard chamfer matching approach, where it shows considerable performance benefits, as well as the feasibility of global localization based on sparse building outline data.
8 citations
01 Jun 2017
TL;DR: A virtual angle measurement soft sensing technique based on the information conversion of an optoelectronic signal provided by an optical scanning system through an FPGA behaving as an actuator and or the controller of actuators is introduced.
Abstract: Mathematical methods for angle measurement have been developed since the beginning of civilization of mankind for multiple daily activities, in specially to solve engineering problems. At the present time thanks to the electronics devices development a big variety of sophisticated transducers and application specific integrated circuits (ASIC) as well as field programmable gate arrays (FPGA), it is possible and increasingly common the development of virtual sensors based on soft sensing for physical magnitudes measurement. This manuscript introduces a virtual angle measurement soft sensing technique based on the information conversion of an optoelectronic signal provided by an optical scanning system (OSS) through an FPGA behaving as an actuator and or the controller of actuators. Obtaining successfully results from the proposed technique matching with the previously OSS mathematical model parameters to calculate an estimate the physical magnitude under interest.
2 citations
Cites background from "Dependence maximization localizatio..."
...e) Structural Health Monitoring, where optical linear variable displacement virtual sensors serve as a viable non-contact alternative to physical measurements of structure displacement [18-22], to mention some....
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01 Jan 2018
TL;DR: The estimation of spatial relations between objects using Probabilistic Logic, including a novel inference method for Markov Logic Networks, and the benefits of combining different sources of semantic information with sensor data are shown in a scene interpretation and a semantic localization task.
Abstract: Increasing autonomy and interactivity of mobile robotic technologies require the inclusion of semantic information in environment representations. This thesis focuses on representations and methods for semantic mapping in particular for urban environments. It covers the estimation of spatial relations between objects using Probabilistic Logic, including a novel inference method for Markov Logic Networks. Moreover, the benefits of combining different sources of semantic information with sensor data are shown in a scene interpretation and a semantic localization task.
2 citations
Cites background from "Dependence maximization localizatio..."
...Irie, Sugiyama, and Tomono [80] present a localization mechanism on highlevel street maps, which contain street as well as sidewalk outlines, that relies on labeling streets in images and retrieving a matching map position using a dependence maximisation approach....
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References
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TL;DR: An overview is presented of the medical image processing literature on mutual-information-based registration, an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application.
Abstract: An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.
3,121 citations
"Dependence maximization localizatio..." refers background or methods in this paper
...Medical image registration is one of the well-known applications of MI.[12] In our method, we employ SMI [17] rather than the original MI to measure the dependence between the sensor data and map....
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...Instead, sensor observations are directly matched to a map using a variant of MI....
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...MaximizingMI is known to be effective for this kind of multi-modal image registration [12] because MI can measure non-linear dependence....
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...Weemploy least squaresmutual information (LSMI) [18] to estimate SMI....
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TL;DR: The OpenStreetMap project is a knowledge collective that provides user-generated street maps that follow the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes.
Abstract: The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.
2,487 citations
"Dependence maximization localizatio..." refers methods in this paper
...We employedGoogleMapsTM in this paper because, for our target area, it provided significantly more accurate sidewalk information than OpenStreetMap....
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...Although other maps such as OpenStreetMap [25] can also be used, geometrically accurate maps are preferred....
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TL;DR: A more robust algorithm is developed called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation of Monte Carlo Localization algorithms, and is applied to mobile robots equipped with range finders.
1,945 citations
"Dependence maximization localizatio..." refers methods in this paper
...This problem is also known as position tracking and has been intensively studied for decades.[23,24] At present, methods fusing a robot’s motion estimation and observations using a particle filter are widely used....
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Journal Article•
1,454 citations
01 Jun 1991
TL;DR: An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed.
Abstract: The application of the extended Kaman filter to the problem of mobile robot navigation in a known environment is presented. An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed. The algorithm is based on an extended Kalman filter that utilizes matches between observed geometric beacons and an a priori map of beacon locations. Two implementations of this navigation algorithm, both of which use sonar, are described. The first implementation uses a simple vehicle with point kinematics equipped with a single rotating sonar. The second implementation uses a 'Robuter' mobile robot and six static sonar transducers to provide localization information while the vehicle moves at typical speeds of 30 cm/s. >
1,394 citations
"Dependence maximization localizatio..." refers methods in this paper
...This problem is also known as position tracking and has been intensively studied for decades.[23,24] At present, methods fusing a robot’s motion estimation and observations using a particle filter are widely used....
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