Large-Scale Direct SLAM with Stereo Cameras

Jakob Engel, Jörg Stückler, Daniel Cremers
International Conference on Intelligent Robots and Systems (IROS), 2015

We propose a novel Large-Scale Direct SLAM algorithm for stereo cameras (Stereo LSD-SLAM) that runs in real-time at high frame rate on standard CPUs. In contrast to sparse interest-point based methods, our approach aligns images directly based on the photoconsistency of all high- contrast pixels, including corners, edges and high texture areas. It concurrently estimates the depth at these pixels from two types of stereo cues: Static stereo through the fixed-baseline stereo camera setup as well as temporal multi-view stereo exploiting the camera motion. By incorporating both disparity sources, our algorithm can even estimate depth of pixels that are under-constrained when only using fixed-baseline stereo. Using a fixed baseline, on the other hand, avoids scale-drift that typically occurs in pure monocular SLAM. We furthermore propose a robust approach to enforce illumination invariance, capable of handling aggressive brightness changes between frames – greatly improving the performance in realistic settings. In experiments, we demonstrate state-of-the-art results on stereo SLAM benchmarks such as Kitti or challenging datasets from the EuRoC Challenge 3 for micro aerial vehicles.

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@string{iros="International Conference on Intelligent Robots and Systems (IROS)"}
author = {J. Engel and J. Stueckler and D. Cremers},
title = {Large-Scale Direct SLAM with Stereo Cameras},
booktitle = iros,
year = 2015,
month = sept,

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