Rapid and robust large-scale SLAM mapping
Determine algorithmic and system-level approaches for Simultaneous Localization and Mapping (SLAM) that enable rapid and robust mapping of large-scale environments, overcoming the limitations observed in single-agent SLAM systems.
References
While single-agent SLAM has seen remarkable progress, the task of mapping large-scale environments quickly and robustly remains an open problem.
— A Survey on Collaborative SLAM with 3D Gaussian Splatting
(2510.23988 - Xuan et al., 28 Oct 2025) in Introduction (Section 1)
It is therefore unclear whether global SLAM optimization actually limits drift in this setting.
— Evaluation of Monocular SLAM Systems on High-Altitude Nadir UAV Footage
(2608.18632 - Spagnolo et al., 19 Aug 2026) in Section 1, Introduction
Despite these successes, achieving reliable and efficient performance in large-scale underground environments remains an open challenge, primarily due to the limitations of onboard computation and memory required for autonomous operation.
— SUPER ODOMETRY 2.0: Resilient Odometry via Hierarchical Adaptation
(2608.25427 - Zhao et al., 26 Aug 2026) in Supplementary Materials, section “Experiments,” subsection “DARPA Subterranean Challenge”