Ben Bellekens | University of Antwerp (original) (raw)

Papers by Ben Bellekens

Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight cam... more Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight camera, is a challenging task with important applications in robotics and computer vision. Due to the recent advent of cheap depth sensing devices, many different 3D registration algorithms have been proposed in literature, focussing on different domains such as localization and mapping or image registration. In this survey paper, we review the state-of-the-art registration algorithms and discuss their common mathematical foundation. Starting from simple deterministic methods, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), more recently introduced approaches such as Iterative Closest Point (ICP) and its variants, are analyzed and compared. The main contribution of this paper therefore consists of an overview of registration algorithms that are of interest in the field of computer vision and robotics, for example Simultaneous Localization and Mapping.

Recently, researchers have tried to solve the computational intensive three-dimensional obstacle ... more Recently, researchers have tried to solve the computational intensive three-dimensional obstacle avoidance by creating a 2D map from a 3D map or by creating a 2D map with multiple altitude levels. When a robot can move in a three-dimensional space, these techniques are no longer sufficient. This paper proposes a new algorithm for realtime three-dimensional obstacle avoidance. This algorithm is based on the 2D VFH+ obstacle avoidance algorithm and uses the octomap framework to represent the three-dimensional environment. The algorithm will generate a 2D Polar Histogram from this octomap which will be used to generate a robot motion. The results show that the robot is able to avoid 3D obstacles in real-time. The algorithm is able to calculate a new robot motion with an average time of 300 µs.

Advanced user interface sensors are able to observe the environment in three dimensions with the ... more Advanced user interface sensors are able to observe the environment in three dimensions with the use of specific optical techniques such as time-of-flight, structured light or stereo vision. Due to the success of modern sensors, which are able to fuse depth and color information of the environment, a new focus on different domains appears. This survey studies different state-of-the-art registration algorithms, which are able to determine the motion between two corresponding 3D point clouds. This survey starts from a mathematical field of view by explaining two deterministic methods, namely Principle Component Analysis (PCA) and Singular Value Decomposition (SVD), towards more iteratively methods such as Iterative Closest Point (ICP) and its variants. We compare the performance of the different algorithms to their precision and robustness based on a real world dataset. The main contribution of this survey consists of the performance benchmark that is based on a real world dataset, wh...

Recently, researchers have tried to solve the computational intensive three-dimensional obstacle ... more Recently, researchers have tried to solve the computational intensive three-dimensional obstacle avoidance by creating a 2D map from a 3D map or by creating a 2D map with multiple altitude levels. When a robot can move in a three-dimensional space, these techniques are no longer sufficient. This paper proposes a new algorithm for realtime three-dimensional obstacle avoidance. This algorithm is based on the 2D VFH+ obstacle avoidance algorithm and uses the octomap framework to represent the three-dimensional environment. The algorithm will generate a 2D Polar Histogram from this octomap which will be used to generate a robot motion. The results show that the robot is able to avoid 3D obstacles in real-time. The algorithm is able to calculate a new robot motion with an average time of 300 µs.

Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight cam... more Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight camera, is a challenging task with important applications in robotics and computer vision. Due to the recent advent of cheap depth sensing devices, many different 3D registration algorithms have been proposed in literature, focussing on different domains such as localization and mapping or image registration. In this survey paper, we review the state-of-the-art registration algorithms and discuss their common mathematical foundation. Starting from simple deterministic methods, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), more recently introduced approaches such as Iterative Closest Point (ICP) and its variants, are analyzed and compared. The main contribution of this paper therefore consists of an overview of registration algorithms that are of interest in the field of computer vision and robotics, for example Simultaneous Localization and Mapping.

Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight cam... more Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight camera, is a challenging task with important applications in robotics and computer vision. Due to the recent advent of cheap depth sensing devices, many different 3D registration algorithms have been proposed in literature, focussing on different domains such as localization and mapping or image registration. In this survey paper, we review the state-of-the-art registration algorithms and discuss their common mathematical foundation. Starting from simple deterministic methods, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), more recently introduced approaches such as Iterative Closest Point (ICP) and its variants, are analyzed and compared. The main contribution of this paper therefore consists of an overview of registration algorithms that are of interest in the field of computer vision and robotics, for example Simultaneous Localization and Mapping.

Recently, researchers have tried to solve the computational intensive three-dimensional obstacle ... more Recently, researchers have tried to solve the computational intensive three-dimensional obstacle avoidance by creating a 2D map from a 3D map or by creating a 2D map with multiple altitude levels. When a robot can move in a three-dimensional space, these techniques are no longer sufficient. This paper proposes a new algorithm for realtime three-dimensional obstacle avoidance. This algorithm is based on the 2D VFH+ obstacle avoidance algorithm and uses the octomap framework to represent the three-dimensional environment. The algorithm will generate a 2D Polar Histogram from this octomap which will be used to generate a robot motion. The results show that the robot is able to avoid 3D obstacles in real-time. The algorithm is able to calculate a new robot motion with an average time of 300 µs.

Advanced user interface sensors are able to observe the environment in three dimensions with the ... more Advanced user interface sensors are able to observe the environment in three dimensions with the use of specific optical techniques such as time-of-flight, structured light or stereo vision. Due to the success of modern sensors, which are able to fuse depth and color information of the environment, a new focus on different domains appears. This survey studies different state-of-the-art registration algorithms, which are able to determine the motion between two corresponding 3D point clouds. This survey starts from a mathematical field of view by explaining two deterministic methods, namely Principle Component Analysis (PCA) and Singular Value Decomposition (SVD), towards more iteratively methods such as Iterative Closest Point (ICP) and its variants. We compare the performance of the different algorithms to their precision and robustness based on a real world dataset. The main contribution of this survey consists of the performance benchmark that is based on a real world dataset, wh...

Recently, researchers have tried to solve the computational intensive three-dimensional obstacle ... more Recently, researchers have tried to solve the computational intensive three-dimensional obstacle avoidance by creating a 2D map from a 3D map or by creating a 2D map with multiple altitude levels. When a robot can move in a three-dimensional space, these techniques are no longer sufficient. This paper proposes a new algorithm for realtime three-dimensional obstacle avoidance. This algorithm is based on the 2D VFH+ obstacle avoidance algorithm and uses the octomap framework to represent the three-dimensional environment. The algorithm will generate a 2D Polar Histogram from this octomap which will be used to generate a robot motion. The results show that the robot is able to avoid 3D obstacles in real-time. The algorithm is able to calculate a new robot motion with an average time of 300 µs.

Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight cam... more Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight camera, is a challenging task with important applications in robotics and computer vision. Due to the recent advent of cheap depth sensing devices, many different 3D registration algorithms have been proposed in literature, focussing on different domains such as localization and mapping or image registration. In this survey paper, we review the state-of-the-art registration algorithms and discuss their common mathematical foundation. Starting from simple deterministic methods, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), more recently introduced approaches such as Iterative Closest Point (ICP) and its variants, are analyzed and compared. The main contribution of this paper therefore consists of an overview of registration algorithms that are of interest in the field of computer vision and robotics, for example Simultaneous Localization and Mapping.