A new polynomial based SLAM algorithm for a mobile robot in an unknown indoor environment (original) (raw)
Related papers
2013 16th International Conference on Advanced Robotics (ICAR), 2013
The present paper faces the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A set of segments is used to model the robot surrounding environment and segments' starting and ending points are used as SLAM landmarks. A segment based mapping algorithm is proposed and used along with an Extended Kalman filter driven by measurements taken by ultrasonic sensors located on the robot. The proposed SLAM algorithm has been tested in both simulated and real experiments yielding to encouraging estimation and mapping results.
A polynomial based SLAM algorithm for mobile robots using ultrasonic sensors - Experimental results
2013 16th International Conference on Advanced Robotics (ICAR), 2013
In this work the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot placed in an unknown indoor environment is faced. The environment is modeled as a set of polynomials used as SLAM landmarks. A polynomial based mapping algorithm is proposed and used along with an Extended Kalman filter to yield a solution to the SLAM problem. The filter updates the robot position and orientation estimation and the environment mapping using sparse measurements taken from a set of on board ultrasonic sensors. The proposed algorithm has been evaluated in both numerical and experimental tests obtaining very good estimation and mapping results.
SLAM process using Polynomial Extended Kalman Filter: Experimental assessment
2008
This paper deals with the simultaneous localization and map building (SLAM) problem using an implementation of the polynomial extended Kalman filter (PEKF). The proposed PEKF implementation is a filtering algorithm which is a polynomial transformation of state evolution and measurement equations. The performances of the algorithm have been evaluated through simulations. The comparison with the standard extended Kalman filter shows that the PEKF provides more consistent estimates in a SLAM framework. Experiments on real data are presented too.
Mobile Robot Localization based on a Polynomial Approach
2007
This paper introduces a new analytical algorithm to perform the localization of a mobile robot using odometry and laser readings. Based on a polynomial approach, the proposed algorithm provides the optimal affine filter in a class of suitably defined estimators. The performance of the algorithm has been evaluated through simulations. The comparison with the standard Extended Kalman Filter shows that the proposed filter provides good estimates also in critical situations where the system nonlinearities cause a bad behavior of the EKF.
This thesis addresses the Simultaneous Localization and Mapping (SLAM) problem, a key problem for any truly autonomous mobile robot. The task for the robot is to build a map of its environment and simultaneously determine its own position in the map while moving. The problem is examined from an estimation-theoretic perspective. The focus is on the core estimation algorithm which provides an estimate for the map and robot pose from two sensor inputs: The first sensor is odometry, i.e. the observation of the robot's movement from the revolution of its wheels. The second is the observation of environment features, so called landmarks. The optimal solution based on maximum likelihood or least square estimation needs excessive computation time, i.e. O((n + p) 3) for n landmarks and p robot poses. Popular approaches like Extended Kalman Filter (EKF) are more efficient but still need O(n 2) computation time and suffer from linearization errors.
This thesis addresses the Simultaneous Localization and Mapping (SLAM) problem, a key problem for any truly autonomous mobile robot. The task for the robot is to build a map of its environment and simultaneously determine its own position in the map while moving. The problem is examined from an estimation-theoretic perspective. The focus is on the core estimation algorithm which provides an estimate for the map and robot pose from two sensor inputs: The first sensor is odometry, i.e. the observation of the robot's movement from the revolution of its wheels. The second is the observation of environment features, so called landmarks. The optimal solution based on maximum likelihood or least square estimation needs excessive computation time, i.e. O((n + p) 3) for n landmarks and p robot poses. Popular approaches like Extended Kalman Filter (EKF) are more efficient but still need O(n 2) computation time and suffer from linearization errors.
This thesis addresses the Simultaneous Localization and Mapping (SLAM) problem, a key problem for any truly autonomous mobile robot. The task for the robot is to build a map of its environment and simultaneously determine its own position in the map while moving. The problem is examined from an estimation-theoretic perspective. The focus is on the core estimation algorithm which provides an estimate for the map and robot pose from two sensor inputs: The first sensor is odometry, i.e. the observation of the robot's movement from the revolution of its wheels. The second is the observation of environment features, so called landmarks. The optimal solution based on maximum likelihood or least square estimation needs excessive computation time, i.e. O((n + p) 3) for n landmarks and p robot poses. Popular approaches like Extended Kalman Filter (EKF) are more efficient but still need O(n 2) computation time and suffer from linearization errors.
A Fully Autonomous Indoor Mobile Robot using SLAM
This paper presents a complete Simultaneous Localization and Mapping (SLAM) solution for indoor mobile robots, addressing feature extraction, autonomous exploration and navigation using the continuously updating map. The platform used is Pioneer PeopleBot equipped with SICK Laser Measurment System (LMS) and odometery. Our algorithm uses Hough Transform to extract the major representative features of indoor environment such as lines and edges. Localization is accomplished using Relative Filter which depends directly on the perception model for the correction of error in the robot state. Our map for localization is in the form of a landmark network whereas for navigation we are using occupancy grid. The resulting algorithm makes the approach computationally lightweight and easy to implement. Finally, we present the results of testing the algorithm in Player/Stage as well as on PeopleBot in our Robotics and Control Lab.
Enhanced SLAM for Autonomous Mobile Robots using Unscented Kalman Filter and Neural Network
Indian Journal of Science and Technology, 2015
A keyword requisite for a correctly autonomous robot is that it can localize itself and carefully map its surroundings simultaneously 1. Many algorithms have been offered for solving SLAM obstacles, for example particle filter, Kalman Filter (KF), Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). The UKF SLAM making a Gaussian noise supposition for the robot observation and its movement. The UKF encourage calculation values analogous to the ones of EKF but does not requirement the linearization of the fundamental model 2. The UKF uses the unscented transform to linearize the movement and measurement models 3. RBF, adaptive to the change of environmental data flowing through the network during the process, can be combined with an UKF to atone for some of the disadvantages of an UKF SLAM method 4. Amir Panah 5 solved the SLAM problem with a neural network based on an Unscented Kalman filter. According to the research results, the UKF SLAM based on Neural Network, shows better performance than the Standard UKF SLAM. Stubberud et al. 6 extended an adaptive EKF composed with neural networks, with a neuro-observer to learn system uncertainties on-line. The offered system boost the performance of a control system comprise uncertainties in the state-estimator's model. In this article, we describe a Hybrid way using RBF network and UKF based SLAM for decline uncertainty in compare to SLAM using Standard UKF. we do, consider the power of UKF based RBF algorithm to handle nonlinear attributes of a mobile robot. We started by introducing some related algorithms on SLAM are explained in section 2, and the Hybrid algorithm is presented in section 3. The detailed description of the simulation and experimental results is shown in Section 4 and finally in Section 5 summarizes the results and gives an outlook on future research activities.
IJERT-Vision and Simultaneous Localization and Mapping based Autonomous Indoor Mobile Robot
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/vision-and-simultaneous-localization-and-mapping-based-autonomous-indoor-mobile-robot https://www.ijert.org/research/vision-and-simultaneous-localization-and-mapping-based-autonomous-indoor-mobile-robot-IJERTV10IS050354.pdf The Simultaneous Localization and Mapping (SLAM) has been one of the most studied subjects in Robotics. It is a fundamental process in mobile robotics, since it is responsible for building maps and at same time estimate the robot position in the environment. In this paper, a real-time implementation of simultaneous localization and mapping (SLAM) is described based on a mobile robot platform, which consists of two driving wheels, one caster wheel, an ultrasonic sensor and a Kinect sensor camera. The robot used for this purpose is differential drive robot. The main algorithm used is extended Kalman Filter (EKF) which is combined with Visual SLAM to handle data association problems and to map the trajectory with more accuracy. A mathematical equations of state space model and observation model is used to represents the motion of a robotic system from one timestep to the next and to infer what the sensor measurement would be at that timestep, respectively. The robot's built-in ability allows it to map the interiors of unknown environment and run autonomously using its self ruling and pilot feature. Robot Operating System is used for data processing and analysis of mobile robot platform. It is intended to implement the proposed SLAM by Extended Kalman Filter and VSLAM using low cost, energy efficient Raspberry PI as embedded system, IMU, laptop and ROS to verify the utility of proposed methods as a indoor wireless mobile robot.