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Papers by Kristiyan Georgiev

Research paper thumbnail of Realtime mapping and scene reconstruction based on mid-level geometric features

Research paper thumbnail of RMSD: A 3D Real-Time Mid-level Scene Description System

Cognitive Systems Monographs, 2015

This paper introduces a system for real-time, visual 3D scene description. A scene is described b... more This paper introduces a system for real-time, visual 3D scene description. A scene is described by planar patches and conical objects (cylinders, cones and spheres). The system makes use of sensor's natural point order, dimensionality reduction and fast incremental model update (in O(1)) to first build 2D geometric features. These features approximate the original data and form candidate sets of possible 3D object models. The candidate sets are used by a region growing algorithm to extract all targeted 3D objects. This two step (raw data to 2D features to 3D objects) approach is able to process 30 frames per second on Kinect depth data, which allows for real-time tracking and feature based robot mapping based on 3D range data.

Research paper thumbnail of Real-time 3D scene description using Spheres, Cones and Cylinders

ArXiv, 2016

The paper describes a novel real-time algorithm for finding 3D geometric primitives (cylinders, c... more The paper describes a novel real-time algorithm for finding 3D geometric primitives (cylinders, cones and spheres) from 3D range data. In its core, it performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model. We use a three stage approach.The first step inspects 1.5D sub spaces, to find ellipses. The next stage uses these ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidate ellipses are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. This allows the approach to process 30 frames per second on Kinect depth data, which suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping.

Research paper thumbnail of Fast plane extraction in 3D range data based on line segments

Fast Plane Extraction in 3D Range Data Based on Line Segments, Sep 2011

This paper describes a fast plane extraction algorithm for 3D range data. Taking advantage of the... more This paper describes a fast plane extraction algorithm for 3D range data. Taking advantage of the point neighborhood structure in data acquired from 3D sensors like range cameras, laser range finders and Microsoft Kinect, it divides the plane-segment extraction task into three steps. The first step is a 2D line segment extraction from raw sensor data, interpreted as 2D data, followed by a line segment based connected component search. The final step finds planes based on connected segment component sets. The first step inspects 2D sub spaces only, leading to a line segment representation of the 3D scan. Connected components of segments represent candidate sets of coplanar segments. Line segment representation and connected components vastly reduce the search space for the plane-extraction step. A region growing algorithm is utilized to find coplanar segments and their optimal (least square error) plane approximation. Region growing contains a fast plane update technique in its core, which combines sets of co-planar segments to form planar elements. Experiments are performed on real world data from different sensors.

Research paper thumbnail of 3D data classification based on mid-level geometric features

Advanced Robotics (ICAR), 2011 …, 2011

This paper introduces an approach to classify robot environments based on planar segments extract... more This paper introduces an approach to classify robot environments based on planar segments extracted from 3D data. In a preprocessing step, point data from a 3D range sensor is transformed to planar patches, i.e. raw data is transformed to a mid level geometric representation. This step allows for a robust, simple and straightforward feature extraction. The features are fed into a learning algorithm, resulting in binary classification into two different types of indoor environments, hallways and office spaces. The main contribution of this paper is to demonstrate the robustness of using mid-level geometric features. Tested on multiple learning algorithms with standard parameters, this approach achieves promising results.

Research paper thumbnail of A hybrid approach to 2D robotic map evaluation

Proceedings of the Workshop on …, 2012

Abstract This article introduces the Temple Map Evaluation Toolkit (TMET), which is a tool for ev... more Abstract This article introduces the Temple Map Evaluation Toolkit (TMET), which is a tool for evaluating robotic maps produced by existing mapping algorithms. The toolkit performs ground truth based evaluation, ie it compares similarities between a map defined as ground truth and a target map. TMET allows for hybrid evaluation, since methods for pose based as well as grid based evaluation are implemented. For pose based evaluation, the user can define regions on the ground truth map which are handled as transformable sub-maps. ...

Research paper thumbnail of Realtime mapping and scene reconstruction based on mid-level geometric features

Research paper thumbnail of RMSD: A 3D Real-Time Mid-level Scene Description System

Cognitive Systems Monographs, 2015

This paper introduces a system for real-time, visual 3D scene description. A scene is described b... more This paper introduces a system for real-time, visual 3D scene description. A scene is described by planar patches and conical objects (cylinders, cones and spheres). The system makes use of sensor's natural point order, dimensionality reduction and fast incremental model update (in O(1)) to first build 2D geometric features. These features approximate the original data and form candidate sets of possible 3D object models. The candidate sets are used by a region growing algorithm to extract all targeted 3D objects. This two step (raw data to 2D features to 3D objects) approach is able to process 30 frames per second on Kinect depth data, which allows for real-time tracking and feature based robot mapping based on 3D range data.

Research paper thumbnail of Real-time 3D scene description using Spheres, Cones and Cylinders

ArXiv, 2016

The paper describes a novel real-time algorithm for finding 3D geometric primitives (cylinders, c... more The paper describes a novel real-time algorithm for finding 3D geometric primitives (cylinders, cones and spheres) from 3D range data. In its core, it performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model. We use a three stage approach.The first step inspects 1.5D sub spaces, to find ellipses. The next stage uses these ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidate ellipses are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. This allows the approach to process 30 frames per second on Kinect depth data, which suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping.

Research paper thumbnail of Fast plane extraction in 3D range data based on line segments

Fast Plane Extraction in 3D Range Data Based on Line Segments, Sep 2011

This paper describes a fast plane extraction algorithm for 3D range data. Taking advantage of the... more This paper describes a fast plane extraction algorithm for 3D range data. Taking advantage of the point neighborhood structure in data acquired from 3D sensors like range cameras, laser range finders and Microsoft Kinect, it divides the plane-segment extraction task into three steps. The first step is a 2D line segment extraction from raw sensor data, interpreted as 2D data, followed by a line segment based connected component search. The final step finds planes based on connected segment component sets. The first step inspects 2D sub spaces only, leading to a line segment representation of the 3D scan. Connected components of segments represent candidate sets of coplanar segments. Line segment representation and connected components vastly reduce the search space for the plane-extraction step. A region growing algorithm is utilized to find coplanar segments and their optimal (least square error) plane approximation. Region growing contains a fast plane update technique in its core, which combines sets of co-planar segments to form planar elements. Experiments are performed on real world data from different sensors.

Research paper thumbnail of 3D data classification based on mid-level geometric features

Advanced Robotics (ICAR), 2011 …, 2011

This paper introduces an approach to classify robot environments based on planar segments extract... more This paper introduces an approach to classify robot environments based on planar segments extracted from 3D data. In a preprocessing step, point data from a 3D range sensor is transformed to planar patches, i.e. raw data is transformed to a mid level geometric representation. This step allows for a robust, simple and straightforward feature extraction. The features are fed into a learning algorithm, resulting in binary classification into two different types of indoor environments, hallways and office spaces. The main contribution of this paper is to demonstrate the robustness of using mid-level geometric features. Tested on multiple learning algorithms with standard parameters, this approach achieves promising results.

Research paper thumbnail of A hybrid approach to 2D robotic map evaluation

Proceedings of the Workshop on …, 2012

Abstract This article introduces the Temple Map Evaluation Toolkit (TMET), which is a tool for ev... more Abstract This article introduces the Temple Map Evaluation Toolkit (TMET), which is a tool for evaluating robotic maps produced by existing mapping algorithms. The toolkit performs ground truth based evaluation, ie it compares similarities between a map defined as ground truth and a target map. TMET allows for hybrid evaluation, since methods for pose based as well as grid based evaluation are implemented. For pose based evaluation, the user can define regions on the ground truth map which are handled as transformable sub-maps. ...