This paper describes a modification in a Continuous Attractor Neural Network (CANN) applied to the Simultaneous Localization and Mapping (SLAM) problem to deal with water currents. The main idea is to improve the prev...
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ISBN:
(纸本)9781509036578
This paper describes a modification in a Continuous Attractor Neural Network (CANN) applied to the Simultaneous Localization and Mapping (SLAM) problem to deal with water currents. The main idea is to improve the previously proposed method called DolphinSLAM to handle with this problem. Since this kind of network is the foundation of bio-inspired SLAM approaches, we evaluate how this common phenomena can affect the position estimation. We demonstrated our proposal using simulated data obtained by using the Underwater Simulator UWSIM.
The use of robots for underwater exploration has increased around the world in the last years. The automation of the underwater monitoring, inspection, and maintenance tasks often requires a mapping and localization s...
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ISBN:
(纸本)9781467397254
The use of robots for underwater exploration has increased around the world in the last years. The automation of the underwater monitoring, inspection, and maintenance tasks often requires a mapping and localization system. One of the key issues of such system is to be able to recognize previously visited places through the sensory information. This paper proposes a modified method for description and recognition of acoustic images. The method builds a graph of the Gaussian probability density function that represents both the shape and the topological relation among the images. It was originally proposed by the first author and his co-authors in 2015. The modifications proposed herein include a new segmentation step for automatic estimation of parameters and a new graph comparison approach that does not need to make use of the orientation of the segmented regions of the images. The new approach was evaluated in a real experiment in a harbor area. It proved to be less dependent on the input parameters than the previous approach.
This paper presents the bio-inspired underwater 3D SLAM algorithm called DolphinSLAM. First, every module of the DolphinSLAM algorithm is explained. Then, the effects of parameter variations regarding the parameters o...
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ISBN:
(纸本)9781467397254
This paper presents the bio-inspired underwater 3D SLAM algorithm called DolphinSLAM. First, every module of the DolphinSLAM algorithm is explained. Then, the effects of parameter variations regarding the parameters of the DolphinSLAM algorithm are investigated based on the use of the Underwater Simulator (UWSim). The parameters of interest are i) the image feature extractors, ii) the vocabulary size regarding the BoW model, and iii) the equations for experience map correction.
The interest in change detection techniques for autonomous robots has increased considerably during recent years. This is partly due to the fact that changes in robot's working environments are relevant for most t...
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The interest in change detection techniques for autonomous robots has increased considerably during recent years. This is partly due to the fact that changes in robot's working environments are relevant for most tasks and robotics applications. Changes or Novelties are usually detected by comparing the current data acquired by the robot with its previous knowledge (a map or any other model of its surroundings). Gaussian Mixture Models (GMM) have been satisfactorily used for detecting changes in 3D point clouds. However, these methods have drawbacks such as a long computational times and strong dependence on the parameters of the algorithms. In structured environments like offices or homes, it is possible to reduce the number of points to be processed by filtering unlikely-to-change regions of the scene. This paper introduces the concept of Vertical Surface Normal Histogram (VSNH). VSNH provides a method for removing from the point clouds acquired those points associated to the main planes: ceiling, walls and floor. Removing these points decreases the size of the problem and improves the segmentation of the environment into Gaussian Mixture Models. The experimental results demonstrate that the proposed method based on GMM and VSNH achieves change detection in structured indoor environments faster and more accurately than previous approaches.
Nowadays, the advance of the technology allows robots to acquire dense point clouds decreasing the price and increasing the performance. However, it is a hard task to deal with due to the large amount of points, the r...
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Nowadays, the advance of the technology allows robots to acquire dense point clouds decreasing the price and increasing the performance. However, it is a hard task to deal with due to the large amount of points, the redundancy and the noise. This paper proposes an adaptable system to build a 3D feature model of point clouds using Gaussian Mixture Models. These 3D models are used in order to detect changes in the autonomous robot's working environment. The presented work describes an efficient change detection system based on two consecutive stages. First, a top-down approach estimates features using Gaussian Mixture Models. The presented new approach improves the performance of previous related works in terms of computational load and robustness, nevertheless the system is selection criteria dependent. Thus, the efficiency of different selection criteria are evaluated and compared in this paper. Experimental results demonstrate that the Minimum Distance Length (MDL) criteria outperforms the other studied methods. In the second stage, a change detection method is performed using the previously estimate Mixture of Gaussians. The proposed full system is able to detect changes using Gaussian Mixture Models with a reduced computational cost in relation to state-of-art algorithms.
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