用2D激光雷达+CNN检测提升室内定位精度，误差降21%
MDF: Multi-Modal Data Fusion with CNN-Based Object Detection for Enhanced Indoor Localization Using LiDAR-SLAM
- 融合2D激光雷达与CNN目标检测，优化SLAM定位
- 绝对轨迹误差降低21.03%，位置误差仅-0.884米
- 适合应急救援、工业自动化等高精度场景
室内定位在无GPS环境下长期面临高精度挑战。本文提出一种基于手持式2D LiDAR与IMU传感器的新型定位系统，实现高动态精度建图、计算高效及实时适应能力。相比3D LiDAR系统，该方案具备快速处理、低成本可扩展性与强鲁棒性，适用于应急响应、自主导航与工业自动化。系统结合基于CNN的对象检测框架，并通过ROS中Cartographer SLAM进行优化，使绝对轨迹误差（ATE）降低21.03%，在与SC-ALOAM等先进方法对比中表现优异，平均x方向位置误差为-0.884米（标准差1.976米）。引入的CNN对象检测增强了在复杂或动态环境下的地图构建与定位鲁棒性，性能优于现有方法26.09%。该系统为复杂室内场景提供了可靠且可扩展的高精度定位解决方案。
原文摘要 · Abstract (English)
Indoor localization faces persistent challenges in achieving high accuracy, particularly in GPS-deprived environments. This study unveils a cutting-edge handheld indoor localization system that integrates 2D LiDAR and IMU sensors, delivering enhanced high-velocity precision mapping, computational efficiency, and real-time adaptability. Unlike 3D LiDAR systems, it excels with rapid processing, low-cost scalability, and robust performance, setting new standards for emergency response, autonomous navigation, and industrial automation. Enhanced with a CNN-driven object detection framework and optimized through Cartographer SLAM (simultaneous localization and mapping ) in ROS, the system significantly reduces Absolute Trajectory Error (ATE) by 21.03%, achieving exceptional precision compared to state-of-the-art approaches like SC-ALOAM, with a mean x-position error of -0.884 meters (1.976 meters). The integration of CNN-based object detection ensures robustness in mapping and localization, even in cluttered or dynamic environments, outperforming existing methods by 26.09%. These advancements establish the system as a reliable, scalable solution for high-precision localization in challenging indoor scenarios
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