Abstract
Pose estimation is one of the fundamental capabilities that must be fulfilled by autonomous vehicles prior to performing other tasks such as collision avoidance and motion control. Due to the complexity of outdoor environments, this problem can only be effectively solved by fusing multiple modalities such as LiDAR and camera. In this paper, we propose a novel method to tightly couple inertial measurements from IMU into the emerging direct visual-laser odometry framework. To be more specific, a 2-step optimization-based approach is employed. Firstly, inertial measurements are used to introduce additional constraints in direct image alignment. The estimated pose is then refined in IMU-assisted windowed refinement. To validate the proposed method, we carry out intensive experiments in two recent and challenging datasets: UrbanLoco and USVInland. Experimental results show that our framework enjoys more robust and accurate pose estimation in challenging scenarios compared to that of existing popular methods.
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Acknowledgement
We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study.
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Pham, QH., Tran, NH., Nguyen, TD. (2023). IMU-Assisted Direct Visual-Laser Odometry in Challenging Outdoor Environments. In: Huang, YP., Wang, WJ., Quoc, H.A., Le, HG., Quach, HN. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2022. Lecture Notes in Networks and Systems, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-031-19694-2_44
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