Simultaneous Localization and Mapping
Also known as SLAM
Overview
SLAM estimates a robot’s changing position and a consistent map at the same time, enabling navigation where GPS or prior maps are unavailable. Systems combine camera, LiDAR, radar, sonar, and inertial measurements using feature or scan matching, sensor-fusion filters, and factor-graph optimization with loop closure to limit accumulated drift. Dense mapping increasingly uses neural fields, semantic scene graphs, and 3D Gaussian splatting, evaluated on indoor RGB-D benchmarks as well as outdoor driving and field-robot datasets. A central challenge is balancing accuracy, dense and useful maps, and real-time resource limits while remaining reliable in dynamic, low-texture, or changing environments.
Latest papers
198 papers20 papers in the last four weeks, down 5% on the four weeks before. 0.1% of all new papers.