Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.08388 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909608822439936 |
|---|---|
| author | Kalan, Saqi Hussain Lee, Boon Giin Chung, Wan-Young |
| author_facet | Kalan, Saqi Hussain Lee, Boon Giin Chung, Wan-Young |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MDF: Multi-Modal Data Fusion with CNN-Based Object Detection for Enhanced Indoor Localization Using LiDAR-SLAM Kalan, Saqi Hussain Lee, Boon Giin Chung, Wan-Young Robotics 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 |
| title | MDF: Multi-Modal Data Fusion with CNN-Based Object Detection for Enhanced Indoor Localization Using LiDAR-SLAM |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.08388 |