Journal ArticleDOI
SLAM++1-A highly efficient and temporally scalable incremental SLAM framework
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TLDR
A general framework for incremental maximum likelihood estimation called SLAM++ is introduced, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate.Abstract:
The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-kno...read more
Citations
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Posted Content
A micro Lie theory for state estimation in robotics
TL;DR: This paper will walk through the most basic principles of the Lie theory, with the aim of conveying clear and useful ideas, and leave a significant corpus of theLie theory behind.
Proceedings ArticleDOI
ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM
TL;DR: This work renovates the numerical solver for VI-SLAM and resolves the global consistency problem that is unaddressed by many state-of-the-art SLAM systems: to guarantee the minimization of re-projection function and inertial constraint function during loop closure.
Posted Content
VDO-SLAM: A Visual Dynamic Object-aware SLAM System
TL;DR: VDO-SLAM is presented, a robust object-aware dynamic SLAM system that exploits semantic information to enable motion estimation of rigid objects in the scene without any prior knowledge of the objects shape or motion models resulting in accurate robot pose and spatio-temporal map estimation.
Journal ArticleDOI
Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance
TL;DR: This article presents an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task, based on a model predictive control framework.
Proceedings ArticleDOI
Dynamic SLAM: The Need For Speed
TL;DR: This paper proposes a new feature-based, model-free, object-aware dynamic SLAM algorithm that exploits semantic segmentation to allow estimation of motion of rigid objects in a scene without the need to estimate the object poses or have any prior knowledge of their 3D models.
References
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Journal ArticleDOI
Vision meets robotics: The KITTI dataset
TL;DR: A novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research, using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras and a high-precision GPS/IMU inertial navigation system.
Proceedings ArticleDOI
Parallel Tracking and Mapping for Small AR Workspaces
Georg Klein,David W. Murray +1 more
TL;DR: A system specifically designed to track a hand-held camera in a small AR workspace, processed in parallel threads on a dual-core computer, that produces detailed maps with thousands of landmarks which can be tracked at frame-rate with accuracy and robustness rivalling that of state-of-the-art model-based systems.
Proceedings ArticleDOI
A benchmark for the evaluation of RGB-D SLAM systems
TL;DR: A large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system is recorded for the evaluation of RGB-D SLAM systems.
Journal ArticleDOI
Approximating discrete probability distributions with dependence trees
TL;DR: It is shown that the procedure derived in this paper yields an approximation of a minimum difference in information when applied to empirical observations from an unknown distribution of tree dependence, and the procedure is the maximum-likelihood estimate of the distribution.
Journal ArticleDOI
A solution for the best rotation to relate two sets of vectors
TL;DR: In this paper, a simple procedure is derived which determines a best rotation of a given vector set into a second vector set by minimizing the weighted sum of squared deviations, which is generalized for any given metric constraint on the transformation.