MD-SLAM: Multi-cue Direct SLAM
Luca Di Giammarino, Leonardo Brizi, Tiziano Guadagnino, Cyrill, Stachniss, Giorgio Grisetti

TL;DR
MD-SLAM is a versatile direct 3D SLAM system that effectively integrates RGB-D and LiDAR sensors, providing accurate localization and mapping without environment assumptions, and is adaptable to different sensor modalities.
Contribution
The paper introduces a generic, multi-cue SLAM pipeline that seamlessly handles RGB-D and LiDAR data with minimal modifications, advancing sensor-agnostic SLAM solutions.
Findings
Performs well on heterogeneous datasets
Outperforms sensor-specific methods in benchmarks
Provides an open-source implementation
Abstract
Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene's structure are often made to maximize estimation accuracy. This paper presents a novel direct 3D SLAM pipeline that works independently for RGB-D and LiDAR sensors. Building upon prior work on multi-cue photometric frame-to-frame alignment, our proposed approach provides an easy-to-extend and generic SLAM system. Our pipeline requires only minor adaptations within the projection model to handle different sensor modalities. We couple a position tracking system with an appearance-based relocalization mechanism that handles large loop closures. Loop closures are validated by the same direct registration…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · 3D Surveying and Cultural Heritage · Advanced Image and Video Retrieval Techniques
