Exploiting Data Parallelism in Graph-Based Simultaneous Localization and Mapping: A Case Study with GPU Accelerations
Author:
Affiliation:
1. Shenyang University of Technology, China
2. Keio University, Japan and Shenyang University of Technology, China
3. Keio University, Japan and RIKEN Center for Computational Science, Japan
Funder
JSPS KAKENHI
JST CREST
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3578178.3578237
Reference24 articles.
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2. Simultaneous localization and mapping (SLAM): part II
3. K. Boikos and C. Bouganis . 2016. Semi-dense SLAM on an FPGA SoC . In Proceedings of the 26th International Conference on Field Programmable Logic and Applications ( Lausanne, Switzerland). 1–4. K. Boikos and C. Bouganis. 2016. Semi-dense SLAM on an FPGA SoC. In Proceedings of the 26th International Conference on Field Programmable Logic and Applications (Lausanne, Switzerland). 1–4.
4. Raffaello Bonghi . 2022. Jetson Stats . https://github.com/rbonghi/jetson_stats Online ; accessed 25 October 2022 . Raffaello Bonghi. 2022. Jetson Stats. https://github.com/rbonghi/jetson_stats Online; accessed 25 October 2022.
5. The SPmap: a probabilistic framework for simultaneous localization and map building
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1. Accelerating Graph-Based SLAM through Data Parallelism and Mixed Precision on FPGAs;2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC);2023-12-18
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