State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment
Currently, almost all robot state estimation and localization systems are based on the Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF). When applying the UKF alone, the estimate of the state is not sufficiently precise. In this paper, a new hierarchical in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/9/4/84 |
| _version_ | 1827704250017251328 |
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| author | Mamadou Doumbia Xu Cheng |
| author_facet | Mamadou Doumbia Xu Cheng |
| author_sort | Mamadou Doumbia |
| collection | DOAJ |
| description | Currently, almost all robot state estimation and localization systems are based on the Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF). When applying the UKF alone, the estimate of the state is not sufficiently precise. In this paper, a new hierarchical infrared navigational algorithm hybridization (HIRNAH) system is developed to provide better state estimation and localization for mobile robots. Two navigation subsystems (inertial navigation system (INS) and, using a novel infrared navigation algorithm (NIRNA), Odom-NIRNA) and an RPLIDAR-A3 scanner cooperation to build HIRNAH. The robot pose (position and orientation) errors are estimated by a system filtering module (SFM) and used to smooth the robot’s final poses. A prototype (two rotary encoders, one smartphone-based robot sensing model and one RPLIDAR-A3 scanner) has been built and mounted on a four-wheeled mobile robot (4-WMR). Simulation results have motivated real-life experiments, and obtained results are compared to some existent research (hardware and control technology navigation (HCTNav), rapid exploring random tree (RRT) and in stand-alone mode (INS)) for performance measurements. The experimental results confirm that HIRNAH presents a more accurate estimation and a lower mean square error (MSE) of the robot’s state than those calculated by the previously cited HCTNav, RRT and INS. |
| first_indexed | 2024-03-10T15:35:05Z |
| format | Article |
| id | doaj.art-ff73d83dbc954fc0ac2f23f62a504627 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T15:35:05Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-ff73d83dbc954fc0ac2f23f62a5046272023-11-20T17:19:56ZengMDPI AGComputers2073-431X2020-10-01948410.3390/computers9040084State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor EnvironmentMamadou Doumbia0Xu Cheng1School of Computer Science and Electronic Engineering, Hunan University, Changsha 420082, ChinaSchool of Computer Science and Electronic Engineering, Hunan University, Changsha 420082, ChinaCurrently, almost all robot state estimation and localization systems are based on the Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF). When applying the UKF alone, the estimate of the state is not sufficiently precise. In this paper, a new hierarchical infrared navigational algorithm hybridization (HIRNAH) system is developed to provide better state estimation and localization for mobile robots. Two navigation subsystems (inertial navigation system (INS) and, using a novel infrared navigation algorithm (NIRNA), Odom-NIRNA) and an RPLIDAR-A3 scanner cooperation to build HIRNAH. The robot pose (position and orientation) errors are estimated by a system filtering module (SFM) and used to smooth the robot’s final poses. A prototype (two rotary encoders, one smartphone-based robot sensing model and one RPLIDAR-A3 scanner) has been built and mounted on a four-wheeled mobile robot (4-WMR). Simulation results have motivated real-life experiments, and obtained results are compared to some existent research (hardware and control technology navigation (HCTNav), rapid exploring random tree (RRT) and in stand-alone mode (INS)) for performance measurements. The experimental results confirm that HIRNAH presents a more accurate estimation and a lower mean square error (MSE) of the robot’s state than those calculated by the previously cited HCTNav, RRT and INS.https://www.mdpi.com/2073-431X/9/4/84odometryINSRPLIDAR-A3 scannertightly hybridization techniqueUKF4-WMR |
| spellingShingle | Mamadou Doumbia Xu Cheng State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment Computers odometry INS RPLIDAR-A3 scanner tightly hybridization technique UKF 4-WMR |
| title | State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment |
| title_full | State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment |
| title_fullStr | State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment |
| title_full_unstemmed | State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment |
| title_short | State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment |
| title_sort | state estimation and localization based on sensor fusion for autonomous robots in indoor environment |
| topic | odometry INS RPLIDAR-A3 scanner tightly hybridization technique UKF 4-WMR |
| url | https://www.mdpi.com/2073-431X/9/4/84 |
| work_keys_str_mv | AT mamadoudoumbia stateestimationandlocalizationbasedonsensorfusionforautonomousrobotsinindoorenvironment AT xucheng stateestimationandlocalizationbasedonsensorfusionforautonomousrobotsinindoorenvironment |