MDF: Multi-Modal Data Fusion with CNN-Based Object Detection for Enhanced Indoor Localization Using LiDAR-SLAM

TL;DR

This paper presents a handheld indoor localization system combining 2D LiDAR, IMU sensors, and CNN-based object detection, achieving high accuracy, real-time performance, and robustness in GPS-denied environments.

Contribution

It introduces a novel multi-modal data fusion approach using CNN-driven object detection with Cartographer SLAM, improving indoor localization accuracy and efficiency over existing methods.

Findings

Reduced Absolute Trajectory Error (ATE) by 21.03%.

Achieved mean x-position error of -0.884 meters.

Outperformed state-of-the-art approaches like SC-ALOAM by 26.09%.

Abstract

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