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GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting

Autor: Xie, Yusen, Huang, Zhenmin, Wu, Jin, Ma, Jun

In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splattin

Externí odkaz: http://arxiv.org/abs/2410.17084

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MCGMapper: Light-Weight Incremental Structure from Motion and Visual Localization With Planar Markers and Camera Groups

Autor: Xie, Yusen, Huang, Zhenmin, Chen, Kai, Zhu, Lei, Ma, Jun

Structure from Motion (SfM) and visual localization in indoor texture-less scenes and industrial scenarios present prevalent yet challenging research topics. Existing SfM methods designed for natural scenes typically yield low accuracy or map-buildin

Externí odkaz: http://arxiv.org/abs/2405.16599

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Collision-Free Trajectory Optimization in Cluttered Environments Using Sums-of-Squares Programming

Autor: Li, Yulin, Zheng, Chunxin, Chen, Kai, Xie, Yusen, Tang, Xindong, Wang, Michael Yu, Ma, Jun

In this work, we propose a trajectory optimization approach for robot navigation in cluttered 3D environments. We represent the robot's geometry as a semialgebraic set defined by polynomial inequalities such that robots with general shapes can be sui

Externí odkaz: http://arxiv.org/abs/2404.05242

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Learning the References of Online Model Predictive Control for Urban Self-Driving

Autor: Wang, Yubin, Peng, Zengqi, Xie, Yusen, Li, Yulin, Ghazzai, Hakim, Ma, Jun

In this work, we propose a novel learning-based model predictive control (MPC) framework for motion planning and control of urban self-driving. In this framework, instantaneous references and cost functions of online MPC are learned from raw sensor d

Externí odkaz: http://arxiv.org/abs/2308.15808

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