Occupancy grid map is a map representation that shows the occupancy of spaces, whether there is any object in a particular area or it is a free space. This map representation is also commonly known as a grid map. However, the accuracy of the occupancy grid map is highly dependent on the accuracy of the sensors. In this paper, low cost and noisy sensors such as infrared sensors were used with the occupancy grid map algorithm integrated with a neural network. The neural network was used to interpret adjacent sensor measurements into cell’s occupancy value in the grid map. From the simulation experiments, it is observed that, that neural network-integrated algorithm has a better map estimate throughout robot’s navigation with mean of 28% more accurate compared to occupancy grid map algorithm without neural network. This finding is beneficial for implementation with simultaneous localization and mapping or commonly known as SLAM problem. This is because SLAM algorithm makes use of both estimations of environment’s map and robot’s state. Thus, a better map estimate throughout the robot’s journey can improve a robot’s state estimate as well.
Partial ob servab ility in EKF b ased mob ile rob ot navigation is investigated in this paper to find a solution that can prevent erroneous estimation. By only considering certain landmarks in an environment, the computational cost in mob ile robot can b e reduced b ut with an increase of uncertainties to the system. This is known as sub optimal condition of the system. Fuzzy Logic technique is proposed to ensure that the estimation achieved desired performance even though some of the landmarks were excluded for references. The Fuzzy Logic is applied to the measurement innovation of Kalman Filter to correct the positions of b oth mob ile rob ot and any ob served landmarks during ob servations. The simulation results shown that the proposed method is capab le to secure reliab le estimation results even a numb er of landmarks b eing excluded from Kalman Filter update process in b oth Gaussian and non -Gaussian noise conditions.
Partial ob servab ility in EKF b ased mob ile rob ot navigation is investigated in this paper to find a solution that can prevent erroneous estimation. By only considering certain landmarks in an environment, the computational cost in mob ile robot can b e reduced b ut with an increase of uncertainties to the system. This is known as sub optimal condition of the system. Fuzzy Logic technique is proposed to ensure that the estimation achieved desired performance even though some of the landmarks were excluded for references. The Fuzzy Logic is applied to the measurement innovation of Kalman Filter to correct the positions of b oth mob ile rob ot and any ob served landmarks during ob servations. The simulation results shown that the proposed method is capab le to secure reliab le estimation results even a numb er of landmarks b eing excluded from Kalman Filter update process in b oth Gaussian and non-Gaussian noise conditions.
<span>This paper analyze the performance of partial observability in simultaneous localization and mapping(SLAM) problem. The study focuses mainly on the effect of having a decorrelation technique known as Covariance Inflation to the estimation. The matrix inversion will be the main element to be investigated through two conditions with respect to some defined environment namely as unstable partially observable SLAM and partially observable SLAM via matrix norm analysis. For assessment purposes, the Extended Kalman Filter estimation is referred as the estimator to understand how the conditions can influence the results. The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.</span>
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