SLAMinDB: Centralized graph databases for mobile robotics

Robotic systems typically require memory recall mechanisms for a variety of tasks including localization, mapping, planning, visualization etc. We argue for a novel memory recall framework that enables more complex inference schemas by separating the computation from its associated data. In this work we propose a shared, centralized data persistence layer that maintains an ensemble of online, situationally-aware robot states. This is realized through a queryable graph-database with an accompanying key-value store for larger data. In turn, this approach is scalable and enables a multitude of capabilities such as experience-based learning and long-term autonomy. Using multi-modal simultaneous localization and mapping and a few example use-cases, we demonstrate the versatility and extensible nature that centralized persistence and SLAMinDB can provide. In order to support the notion of life-long autonomy, we envision robots to be endowed with such a persistence model, enabling them to revisit previous experiences and improve upon their existing task-specific capabilities.

Authors

• Dehann Fourie
• Samuel Claassens
• Sudeep Pillai
• Roxana Mata
• John J. Leonard

Keywords

• Simultaneous localization and mapping
• Computer architecture
• Relational databases
• Navigation
• Graph Database
• Large Data
• Robotic System
• Sensor Data
• Local Processes
• Extraction Efficiency
• Point Cloud
• Local Estimates
• System Database
• Large Volumes Of Data
• Central Database
• Relational Database
• Random Access
• Computing Services
• Query Language
• Robot Navigation
• Network Length
• Loop Closure
• Factor Graph