What question did this study set out to answer?

To improve collaborative simultaneous localization and mapping (SLAM) using optimized map fusion techniques in multi-robot systems.

June 2, 2026International Journal of Bio-Inspired Computation0 citations

Optimised Map Fusion for Collaborative SLAM in Multi-Robot Systems

LRLuigi Maciel Ribeiro NNNadia Nedjah PCPaulo Carvalho

Key Points

To improve collaborative simultaneous localization and mapping (SLAM) using optimized map fusion techniques in multi-robot systems.
Developed new algorithms for map fusion under varying sensor conditions.
Implemented in a multi-robot environment with scenarios simulating real-time navigation.
Analyzed efficiencies using performance metrics like accuracy and processing time.
Increased mapping accuracy by 25% (p<0.05) compared to previous methods.
Reduced computational load by 30%, significantly speeding up processing times.
Enhanced collaborative efficiency among robots, leading to quicker task completion.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Cite This Study

Ribeiro et al. (2026) studied this question.

synapsesocial.com/papers/6a1e72ad30b38c64201b5df0 https://doi.org/10.1504/ijbic.2026.10078908

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June 2, 2026International Journal of Bio-Inspired Computation0 citations

Optimised Map Fusion for Collaborative SLAM in Multi-Robot Systems

LRLuigi Maciel Ribeiro NNNadia Nedjah PCPaulo Carvalho

Key Points

To improve collaborative simultaneous localization and mapping (SLAM) using optimized map fusion techniques in multi-robot systems.
Developed new algorithms for map fusion under varying sensor conditions.
Implemented in a multi-robot environment with scenarios simulating real-time navigation.
Analyzed efficiencies using performance metrics like accuracy and processing time.
Increased mapping accuracy by 25% (p<0.05) compared to previous methods.
Reduced computational load by 30%, significantly speeding up processing times.
Enhanced collaborative efficiency among robots, leading to quicker task completion.

Abstract

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Cite This Study

Ribeiro et al. (2026) studied this question.

synapsesocial.com/papers/6a1e72ad30b38c64201b5df0 https://doi.org/10.1504/ijbic.2026.10078908

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