A ROS2–Based Architecture for Indoor 3D Mapping and Autonomous Navigation with Azure Kinect and LiDAR

Abstract

High-fidelity 3D mapping is a prerequisite for deploying location-based services, particularly for accessible indoor navigation. While low-cost mapping platforms, such as configurations based on Raspberry Pi 4, are common, they present significant limitations in sensor throughput and onboard 3D reconstruction capabilities. This work introduces a new ROS2 Humble–based architecture running on the highperformance NVIDIA Corporation (NVIDIA) Jetson Orin NX platform. The sensing capabilities of the system are substantially enhanced through the integration of the Azure Kinect DK camera, while mapping and autonomous exploration rely on modern algorithms, such as Real-Time Appearance-Based Mapping (RTAB-Map) ROS2 and a custom LiDAR-based exploration script. The proposed architecture overcomes the constraints inherent to previous low-cost designs and enables a unified, efficient, and fully onboard processing pipeline. Functional validation confirms the architecture's capability to generate dense 3D maps and semantic data in real-time. Experimental results demonstrate stable Light Detection and Ranging (LiDAR) processing at 10 Hz and robust odometry updates at 10 Hz, ensuring the environmental detail required for cognitive accessibility applications.