Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order Minimization
Visual–inertial odometry (VIO) is the primary supporting technology for autonomous systems, but it faces three major challenges: initialization sensitivity, dynamic illumination, and multi-sensor fusion. In order to overcome these challenges, this paper proposes stereo direct sparse visual–inertial...
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MDPI AG
2025-08-01
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| author | Chenhui Fu Jiangang Lu |
| author_facet | Chenhui Fu Jiangang Lu |
| author_sort | Chenhui Fu |
| collection | DOAJ |
| description | Visual–inertial odometry (VIO) is the primary supporting technology for autonomous systems, but it faces three major challenges: initialization sensitivity, dynamic illumination, and multi-sensor fusion. In order to overcome these challenges, this paper proposes stereo direct sparse visual–inertial odometry with efficient second-order minimization. It is entirely implemented using the direct method, which includes a depth initialization module based on visual–inertial alignment, a stereo image tracking module, and a marginalization module. Inertial measurement unit (IMU) data is first aligned with a stereo image to initialize the system effectively. Then, based on the efficient second-order minimization (ESM) algorithm, the photometric error and the inertial error are minimized to jointly optimize camera poses and sparse scene geometry. IMU information is accumulated between several frames using measurement preintegration and is inserted into the optimization as an additional constraint between keyframes. A marginalization module is added to reduce the computation complexity of the optimization and maintain the information about the previous states. The proposed system is evaluated on the KITTI visual odometry benchmark and the EuRoC dataset. The experimental results demonstrate that the proposed system achieves state-of-the-art performance in terms of accuracy and robustness. |
| format | Article |
| id | doaj-art-a24b7b129ef14afa97ffdb7a3bf435b7 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a24b7b129ef14afa97ffdb7a3bf435b72025-08-20T03:36:26ZengMDPI AGSensors1424-82202025-08-012515485210.3390/s25154852Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order MinimizationChenhui Fu0Jiangang Lu1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaVisual–inertial odometry (VIO) is the primary supporting technology for autonomous systems, but it faces three major challenges: initialization sensitivity, dynamic illumination, and multi-sensor fusion. In order to overcome these challenges, this paper proposes stereo direct sparse visual–inertial odometry with efficient second-order minimization. It is entirely implemented using the direct method, which includes a depth initialization module based on visual–inertial alignment, a stereo image tracking module, and a marginalization module. Inertial measurement unit (IMU) data is first aligned with a stereo image to initialize the system effectively. Then, based on the efficient second-order minimization (ESM) algorithm, the photometric error and the inertial error are minimized to jointly optimize camera poses and sparse scene geometry. IMU information is accumulated between several frames using measurement preintegration and is inserted into the optimization as an additional constraint between keyframes. A marginalization module is added to reduce the computation complexity of the optimization and maintain the information about the previous states. The proposed system is evaluated on the KITTI visual odometry benchmark and the EuRoC dataset. The experimental results demonstrate that the proposed system achieves state-of-the-art performance in terms of accuracy and robustness.https://www.mdpi.com/1424-8220/25/15/4852direct sparse odometryefficient second-order minimizationmarginalizationsliding window optimization |
| spellingShingle | Chenhui Fu Jiangang Lu Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order Minimization Sensors direct sparse odometry efficient second-order minimization marginalization sliding window optimization |
| title | Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order Minimization |
| title_full | Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order Minimization |
| title_fullStr | Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order Minimization |
| title_full_unstemmed | Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order Minimization |
| title_short | Stereo Direct Sparse Visual–Inertial Odometry with Efficient Second-Order Minimization |
| title_sort | stereo direct sparse visual inertial odometry with efficient second order minimization |
| topic | direct sparse odometry efficient second-order minimization marginalization sliding window optimization |
| url | https://www.mdpi.com/1424-8220/25/15/4852 |
| work_keys_str_mv | AT chenhuifu stereodirectsparsevisualinertialodometrywithefficientsecondorderminimization AT jianganglu stereodirectsparsevisualinertialodometrywithefficientsecondorderminimization |