While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised. Sparse visual SLAM systems have attained high levels of accuracy and robustness through the inclusion of inertial measurements in a tightly-coupled fusion. Inspired by this performance, we propose the first tightly-coupled dense RGB-D-inertial SLAM system. Our system has real-time capability while running on a GPU. It jointly optimises for the camera pose, velocity, IMU biases and gravity direction while building up a globally consistent, fully dense surfel-based 3D reconstruction of the environment. Through a series of experiments on both synthetic and real world datasets, we show that our dense visual-inertial SLAM system is more robust to fast motions and periods of low texture and low geometric variation than a related RGB-D-only SLAM system.
@article{arxiv.2207.10940,
title = {Dense RGB-D-Inertial SLAM with Map Deformations},
author = {Tristan Laidlow and Michael Bloesch and Wenbin Li and Stefan Leutenegger},
journal= {arXiv preprint arXiv:2207.10940},
year = {2022}
}
Comments
Accepted at IROS 2017; supplementary video available at https://youtu.be/-gUdQ0cxDh0