Related references
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Article
Computer Science, Artificial Intelligence
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Summary: This paper introduces a dynamic-environment-robust visual SLAM system named YOLO-SLAM, which effectively reduces the impact of dynamic objects and improves stability and accuracy in highly dynamic environments by utilizing a lightweight object detection network and a new geometric constraint method.
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IEEE SENSORS JOURNAL
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Summary: Autonomous driving is considered a promising solution to protecting humans from serious accidents. 3D object detection plays a crucial role in the perception system, specifically in path planning, motion prediction, and collision avoidance. However, the field of 3D object detection for autonomous driving is still in its early stages, facing challenges such as visual appearance recovery without depth information, representation learning from partially occluded point clouds, and semantic alignments across different modalities. This comprehensive survey aims to address this gap by examining sensors, datasets, performance metrics, and recent state-of-the-art detection methods, along with their advantages and disadvantages. The article also provides quantitative comparisons and case studies, including runtime analysis, error analysis, and robustness analysis.
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IEEE TRANSACTIONS ON ROBOTICS
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Summary: This paper proposes an algorithm that combines dynamic object detection with visual SLAM to enhance the robustness of SLAM in dynamic environments, effectively eliminating the influence of dynamic objects on the algorithm.
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