A mutated FastSLAM using soft computing

Havangi, Ramazan

doi:10.1108/ir-11-2016-0277

Cited by 7 publications

(10 citation statements)

References 23 publications

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“…To realize the scene classification and recognition, ResNet deep residual network is adopted in this paper [32]. Although there are lots of methods such as SVM and fuzzy k-NN, which have been successfully applied in image classification, the performance of these traditional image classification methods cannot satisfy the requirements of the semantic SLAM system [33,34]. So, the deep learning method is adopted in the proposed semantic SLAM system, which is suitable for the complex semantic recognition.…”

Section: Semantic Map Construction Methodmentioning

confidence: 99%

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Gong

et al. 2020

Computational Intelligence and Neuroscience

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The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

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Section: Semantic Map Construction Methodmentioning

confidence: 99%

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Gong

et al. 2020

Computational Intelligence and Neuroscience

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“…The robot moves at a speed 3m/s and with a maximum steering angle of 30 degrees. In addition, the robot has 4 meters wheel base and is equipped with a range-bearing sensor with a maximum range of 20m and a To evaluate the proposed method, its performance is compared with FastSLAM2.0 and improved FastSLAM (IFast-SLAM) [16] for the benchmark environment.…”

Section: Simulationmentioning

confidence: 99%

“…It occurs when likelihood lies in the tail of the proposal distribution [4]. Researchers have been trying to solve these problems in [4], [11][12][13][14][15][16]. In [16], a modi ed FastSLAM1.0 is presented by soft computing.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have been trying to solve these problems in [4], [11][12][13][14][15][16]. In [16], a modi ed FastSLAM1.0 is presented by soft computing. In this algorithm, an adaptive neuro-fuzzy extended Kalman lter is used for landmark feature estimation and a novel multi swarm particle lter is presented to overcome the impoverishment of FastSLAM.…”

Section: Introductionmentioning

confidence: 99%

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Improved FastSLAM2.0 using ANFIS and PSO

Havangi

2016

Automatika

Self Cite

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Original scienti c paper FastSLAM2.0 is a framework for simultaneous localization of robot using a Rao-Blackwellized particle lter (RBPF). One of the problems of FastSLAM2.0 relates to the design of RBPF. The performance and quality of the estimation of RBPF depends heavily on the correct a priori knowledge of the process and measurement noise covariance matrices that are in most real-life applications unknown. On the other hand, an incorrect a priori knowledge may seriously degrade their performance. This paper presents an intelligent RBPF to solve this problem. In this method, two adaptive Neuro-Fuzzy inference systems (ANFIS) are used for tuning the process and measurement noise covariance matrices and for increasing acuuracy and consistency. In addition, we use particle swarm optimization (PSO) to optimize the performance of sampling. Experimental results demonstrate that the proposed algorithm is effective.

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“…Step 5. Registered point clouds are used by an improved FastSLAM 2.0 to complete 3D Occupancy Grid map [18].…”

Section: D Vslam Using a Kinect Sensormentioning

confidence: 99%

3D Visual SLAM Based on Multiple Iterative Closest Point

Tao

Liu

2015

Mathematical Problems in Engineering

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With the development of novel RGB-D visual sensors, data association has been a basic problem in 3D Visual Simultaneous Localization and Mapping (VSLAM). To solve the problem, a VSLAM algorithm based on Multiple Iterative Closest Point (MICP) is presented. By using both RGB and depth information obtained from RGB-D camera, 3D models of indoor environment can be reconstructed, which provide extensive knowledge for mobile robots to accomplish tasks such as VSLAM and Human-Robot Interaction. Due to the limited views of RGB-D camera, additional information about the camera pose is needed. In this paper, the motion of the RGB-D camera is estimated by a motion capture system after a calibration process. Based on the estimated pose, the MICP algorithm is used to improve the alignment. A Kinect mobile robot which is running Robot Operating System and the motion capture system has been used for experiments. Experiment results show that not only the proposed VSLAM algorithm achieved good accuracy and reliability, but also the 3D map can be generated in real time.

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A mutated FastSLAM using soft computing

Cited by 7 publications

References 23 publications

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Improved FastSLAM2.0 using ANFIS and PSO

3D Visual SLAM Based on Multiple Iterative Closest Point

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