Bad slam: Bundle adjusted direct RGB-D slam
Paper i proceeding, 2019

A key component of Simultaneous Localization and Mapping (SLAM) systems is the joint optimization of the estimated 3D map and camera trajectory. Bundle adjustment (BA) is the gold standard for this. Due to the large number of variables in dense RGB-D SLAM, previous work has focused on approximating BA. In contrast, in this paper we present a novel, fast direct BA formulation which we implement in a real-time dense RGB-D SLAM algorithm. In addition, we show that direct RGB-D SLAM systems are highly sensitive to rolling shutter, RGB and depth sensor synchronization, and calibration errors. In order to facilitate state-of-the-art research on direct RGB-D SLAM, we propose a novel, well-calibrated benchmark for this task that uses synchronized global shutter RGB and depth cameras. It includes a training set, a test set without public ground truth, and an online evaluation service. We observe that the ranking of methods changes on this dataset compared to existing ones, and our proposed algorithm outperforms all other evaluated SLAM methods. Our benchmark and our open source SLAM algorithm are available at:

Datasets and Evaluation

RGBD sensors and analytics

3D from Multiview and Sensors


Thomas Schops

Eidgenössische Technische Hochschule Zürich (ETH)

Torsten Sattler

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Marc Pollefeys


Eidgenössische Technische Hochschule Zürich (ETH)

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

10636919 (ISSN)

Vol. 2019-June 134-144 8954208

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Long Beach, USA,


Robotteknik och automation


Datavetenskap (datalogi)



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