Abstract
The ARG-CM-2.0 is a physical, buildable Real-Time Multi-Sensor Spatial Mapping and Localization Core. It generates stable, drift-free 3D maps of environments by fusing data from LiDAR, visual cameras, IMUs, and ultrasonic sensors using Simultaneous Localization and Mapping (SLAM) algorithms accelerated on an FPGA.
In engineering terms, "Cognitive-Mapping Reinforcement" is implemented as a Tightly-Coupled Sensor Fusion Engine. It correlates geometric data (LiDAR/Depth) with semantic data (Visual) and inertial motion (IMU) to create a unified "cognitive map" that remains stable even when individual sensors fail (e.g., visual dropout in low light, LiDAR glare in smoke). This ensures "mapping drift" is suppressed to <0.1% of distance traveled, providing a reliable spatial backbone for dementia support robots, wildfire drones, and multi-domain research platforms.
This document provides the exact specifications, Bill of Materials (BOM), schematics, and FPGA/ARM architecture to construct a prototype that achieves <30ms mapping latency, cm-level localization accuracy, and robust loop closure in dynamic environments.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Caldwell, Michael Victor Mr., "ARG-CM-2.0: Cognitive-Mapping Reinforcement Engine (Physical Prototype Edition) Date: 2026-07-29 Status: Open-Hardware • Builder-Grade • Test-Bench Ready", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11218