Akademik Veri Yönetim Sistemi
2026 12th International Conference on Automation, Robotics and Applications (ICARA), İstanbul, Türkiye, 5 - 07 Şubat 2026, ss.313-318, (Tam Metin Bildiri)
Map completion has been shown to improve mapdependent robotic tasks such
as navigation by predicting unobserved parts of the environment. Most
existing approaches either perform global map completion or predict
local regions around the robot, and evaluate performance using
high-level metrics such as coverage or path length. In contrast, this
paper investigates how local map completion around frontiers affects the
internal behavior of a standard ROS navigation stack. Our goal is not
to introduce a new completion model, but to provide a system-level
evaluation of how frontier-centric completion affects navigation
stability under SLAM noise. To isolate this effect from model-specific
factors, we use an aligned ground-truth occupancy map as an oracle that
emulates a near-perfect completion module. Local patches are extracted
around selected frontiers and fused into the active navigation map. We
study increasingly stronger completion strategies, including global wall
closing and invalid frontier filtering, within a frontier-based
exploration pipeline. We evaluate their impact using navigation-specific
metrics such as global planning cycles and navigation duration. By
treating map completion as a modular capability rather than a specific
model, the proposed methodology provides a model-agnostic diagnostic
framework and an upper bound on the navigation benefits achievable
through local completion in classical ROS pipelines.