Transformation & Translation Occupancy Grid Mapping: 2-dimensional deep learning refined SLAM

SLAM (Simultaneous Localisation and Mapping) is an important component in robotics, providing a map of an environment and enabling localisation and navigation. While 3D LiDAR odometry and mapping systems have advanced in recent years, producing accurate motion estimates and detailed 3D maps, high-quality 2D occupancy grid maps (OGMs) remain challenging to obtain in large, complex indoor environments. OGMs are often degraded by drifts in odometry, sensor artefacts, and partial observability, resulting in maps with fractured walls, double boundaries, and artefacts that limit readability for mapping-centric tasks such as floor plan creation. To address this, we propose Transformation & Translation Occupancy Grid Mapping (TT-OGM), a system-level pipeline that targets map fidelity. TT-OGM leverages 3D scan registration to stabilise 2D map construction via projection and standard occupancy updates, then applies a learned GAN-based refinement module as post-processing to remove artefacts, regularise structure, and complete small missing regions. To enable training at scale, we introduce an offline DRL-based data generation process that produces paired but weakly aligned erroneous/clean OGMs spanning diverse error modes and severities. We demonstrate TT-OGM in real-time on a building-scale dataset collected at Loughborough University and evaluate map fidelity against a registered floor-plan reference using mIoU, masked SSIM, and occupied-boundary F1. We additionally report localisation accuracy on S3Ev2 using translation ATE (RMSE) against Cartographer and SLAM Toolbox (Karto). Our results show that 3D registration improves baseline 2D map quality over standard 2D SLAM outputs, and that GAN refinement further increases structural consistency and boundary accuracy in our pipeline. Additional ablations on synthetic stress tests and qualitative transfer to unseen Radish sequences show that the refinement module consistently improves OGM readability under common noise, moderate drift, and clutter conditions. • System-level Transformation-Translation (TT) pipeline for generating 2D OGMs from 3D LiDAR registration/odometry. • Learned post-processing module that refines OGMs by reducing common mapping artefacts and improving boundary fidelity. • DRL-driven synthetic data generation producing paired degraded/clean OGMs with controllable error modes for training and stress testing.