Abstract
This paper presents the use of Google’s simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help of scheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scans while maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose’s generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy.
Highlights
Cartographer is a simultaneous localization and mapping (SLAM) method developed by Google, which utilizes grid-based mapping together with a Ceres based scan matcher to reconstruct environment across various sensors configuration [1].Initially, it was developed for portable applications to map unknown areas with a person using the inertial measurement unit (IMU) and LiDAR installed in a backpack
It was developed for portable applications to map unknown areas with a person using the inertial measurement unit (IMU) and LiDAR installed in a backpack
This paper contributes to the improvisation of the current Cartographer or SLAM by using an adaptive multistage distance scheduler to reduce computational load while maintaining accuracy
Summary
Cartographer is a SLAM method developed by Google, which utilizes grid-based mapping together with a Ceres based scan matcher to reconstruct environment across various sensors configuration [1]. Since the codes have been made public, Cartographer is managed by an open source community, and various improvements have been made to widen the scope of applications including more substantial map support, more sensor integration, and other technological improvements which support intelligent robots. This paper contributes to the improvisation of the current Cartographer or SLAM by using an adaptive multistage distance scheduler to reduce computational load while maintaining accuracy. Optimizing Cartographer by using an adaptive multistage distance scheduler (AMDS) to increase time computation performance up to 20% while maintaining the pose generation and map accuracy similar to that of standard Cartographer; Integrating Cartographer by using a car for online world mapping, which is crucial for the future autonomous vehicle navigation systems; Appl.
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