A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer
IMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/989 |
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| author | Cheng Liu Tao Wang Zhi Li Peng Tian |
| author_facet | Cheng Liu Tao Wang Zhi Li Peng Tian |
| author_sort | Cheng Liu |
| collection | DOAJ |
| description | IMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of the IMU and visual measurement. The algorithm fuses motion information on the velocity layer, thereby mitigating the excessive accumulation of IMU errors caused by direct subtraction on the positional layer after quadratic integration. Furthermore, by incorporating a weighting mechanism, the algorithm allows for a flexible adjustment of the emphasis placed on IMU data versus visual information, which augments the robustness and adaptability of autonomous motion estimation for robots. The simulation and dataset experiments demonstrate that the ICEKF can provide reliable estimates for robot motion trajectories. |
| format | Article |
| id | doaj-art-a34fb8118fab447283cca26fc3627eab |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a34fb8118fab447283cca26fc3627eab2025-01-24T13:21:35ZengMDPI AGApplied Sciences2076-34172025-01-0115298910.3390/app15020989A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity LayerCheng Liu0Tao Wang1Zhi Li2Peng Tian3State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, ChinaState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, ChinaState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 401100, ChinaIMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of the IMU and visual measurement. The algorithm fuses motion information on the velocity layer, thereby mitigating the excessive accumulation of IMU errors caused by direct subtraction on the positional layer after quadratic integration. Furthermore, by incorporating a weighting mechanism, the algorithm allows for a flexible adjustment of the emphasis placed on IMU data versus visual information, which augments the robustness and adaptability of autonomous motion estimation for robots. The simulation and dataset experiments demonstrate that the ICEKF can provide reliable estimates for robot motion trajectories.https://www.mdpi.com/2076-3417/15/2/989loosely coupled visual–inertial measurementvisual inertial odometryrobot autonomous localizationdata fusion |
| spellingShingle | Cheng Liu Tao Wang Zhi Li Peng Tian A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer Applied Sciences loosely coupled visual–inertial measurement visual inertial odometry robot autonomous localization data fusion |
| title | A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer |
| title_full | A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer |
| title_fullStr | A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer |
| title_full_unstemmed | A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer |
| title_short | A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer |
| title_sort | novel real time autonomous localization algorithm based on weighted loosely coupled visual inertial data of the velocity layer |
| topic | loosely coupled visual–inertial measurement visual inertial odometry robot autonomous localization data fusion |
| url | https://www.mdpi.com/2076-3417/15/2/989 |
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