Improving Sensor Adaptability and Functionality in Cartographer Simultaneous Localization and Mapping

Wonseok Jeong

doi:10.3390/s25061808

Improving Sensor Adaptability and Functionality in Cartographer Simultaneous Localization and Mapping

2025-03-14

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DOI：10.3390/s25061808

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This paper aims to address sensor-related challenges in simultaneous localization and mapping (SLAM) systems, specifically within the open-source Google Cartographer project, which implements graph-based SLAM. The primary problem tackled is the adaptability and functionality of SLAM systems in diverse robotic applications. To solve this, we developed a novel SLAM framework that integrates five additional functionalities into the existing Google Cartographer and Robot Operating System (ROS). These innovations include an inertial data generation system and a sensor data preprocessing system to mitigate issues arising from various sensor configurations. Additionally, the framework enhances system utility through real-time 3D topographic mapping, multi-node SLAM capabilities, and elliptical sensor data filtering. The average execution times for sensor data preprocessing and virtual inertial data generation are 0.55 s and 0.15 milliseconds, indicating a low computational overhead. Elliptical filtering has nearly the same execution speed as the existing filtering scheme.

Keyword:

Cartographer

simultaneous localization and mapping

Robot Operating System

multi-node

real time

Improving Sensor Adaptability and Functionality in Cartographer Simultaneous Localization and Mapping

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