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34
Dense Visual SLAM for RGB-D Cameras
"... Abstract — In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher ..."
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Cited by 22 (4 self)
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Abstract — In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher pose accuracy. Furthermore, we propose an entropy-based similarity measure for keyframe selection and loop closure detection. From all successful matches, we build up a graph that we optimize using the g2o framework. We evaluated our approach extensively on publicly available benchmark datasets, and found that it performs well in scenes with low texture as well as low structure. In direct comparison to several stateof-the-art methods, our approach yields a significantly lower trajectory error. We release our software as open-source. I.
Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions
"... Abstract—The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an R ..."
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Cited by 20 (5 self)
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Abstract—The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an RGB-D sensor. We show that by representing the geometry with a signed distance function (SDF), the camera pose can be efficiently estimated by directly minimizing the error of the depth images on the SDF. As the SDF contains the distances to the surface for each voxel, the pose optimization can be carried out extremely fast. By iteratively estimating the camera poses and integrating the RGB-D data in the voxel grid, a detailed reconstruction of an indoor environment can be achieved. We present reconstructions of several rooms using a hand-held sensor and from onboard an autonomous quadrocopter. Our extensive evaluation on publicly available benchmark data shows that our approach is more accurate and robust than the iterated closest point algorithm (ICP) used by KinectFusion, and yields often a comparable accuracy at much higher speed to feature-based bundle adjustment methods such as RGB-D SLAM for up to medium-sized scenes. I.
A Benchmark for RGB-D Visual Odometry, 3D Reconstruction and SLAM
"... for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth s ..."
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Cited by 7 (1 self)
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for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available. I.
Large-Scale Multi-Resolution Surface Reconstruction from RGB-D Sequences
"... We propose a method to generate highly detailed, textured 3D models of large environments from RGB-D sequences. Our system runs in real-time on a standard desktop PC with a state-of-the-art graphics card. To reduce the memory consumption, we fuse the acquired depth maps and colors in a multi-scale o ..."
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Cited by 6 (1 self)
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We propose a method to generate highly detailed, textured 3D models of large environments from RGB-D sequences. Our system runs in real-time on a standard desktop PC with a state-of-the-art graphics card. To reduce the memory consumption, we fuse the acquired depth maps and colors in a multi-scale octree representation of a signed distance function. To estimate the camera poses, we construct a pose graph and use dense image alignment to determine the relative pose between pairs of frames. We add edges between nodes when we detect loop-closures and optimize the pose graph to correct for long-term drift. Our implementation is highly parallelized on graphics hardware to achieve real-time performance. More specifically, we can reconstruct, store, and continuously update a colored 3D model of an entire corridor of nine rooms at high levels of detail in real-time on a single GPU with 2.5GB. 1.
Dense Semi-Rigid Scene Flow Estimation from RGBD images ⋆
"... Abstract. Scene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piece-wise rigidity of real world scenes. By modeling the motion as a field of twists, our method encour ..."
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Cited by 5 (0 self)
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Abstract. Scene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piece-wise rigidity of real world scenes. By modeling the motion as a field of twists, our method encourages piecewise smooth solutions of rigid body motions. We give a general formulation to solve for local and global rigid motions by jointly using intensity and depth data. In order to deal effi-ciently with a moving camera, we model the motion as a rigid component plus a non-rigid residual and propose an alternating solver. The evalu-ation demonstrates that the proposed method achieves the best results in the most commonly used scene flow benchmark. Through additional experiments we indicate the general applicability of our approach in a variety of different scenarios.
A State of the Art Report on Kinect Sensor Setups in Computer Vision
"... Abstract. During the last three years after the launch of the Microsoft Kinect R ○ in the end-consumer market we have become witnesses of a small revolution in computer vision research towards the use of a standardized consumer-grade RGBD sensor for scene content retrieval. Beside classical localiza ..."
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Cited by 4 (0 self)
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Abstract. During the last three years after the launch of the Microsoft Kinect R ○ in the end-consumer market we have become witnesses of a small revolution in computer vision research towards the use of a standardized consumer-grade RGBD sensor for scene content retrieval. Beside classical localization and motion capturing tasks the Kinect has successfully been employed for the reconstruction of opaque and transparent objects. This report gives a comprehensive overview over the main publications using the Microsoft Kinect out of its original context as a decision-forest based motion-capturing tool. 1
A.: Efficient compositional approaches for real-time robust direct visual odometry from RGB-D data
- In: Intl. Conf. on Intelligent Robot Systems (IROS) (2013
"... Abstract — In this paper we give an evaluation of different methods for computing frame-to-frame motion estimates for a moving RGB-D sensor, by means of aligning two images using photometric error minimization. These kind of algorithms have recently shown to be very accurate and robust and therefore ..."
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Cited by 4 (1 self)
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Abstract — In this paper we give an evaluation of different methods for computing frame-to-frame motion estimates for a moving RGB-D sensor, by means of aligning two images using photometric error minimization. These kind of algorithms have recently shown to be very accurate and robust and therefore provide an attractive solution for robot ego-motion estimation and navigation. We demonstrate three different alignment strategies, namely the Forward-Compositional, the Inverse-Compositional and the Efficient Second-Order Minimization approach, in a general robust estimation framework. We further show how estimating global affine illumination changes, in general improves the performance of the algorithms. We compare our results with recently published work, considered as state-of-the art in this field, and show that our solutions are in general more precise and can perform in real-time on standard hardware. I.
Tracking an RGB-D Camera Using Points and Planes
, 2013
"... Planes are dominant in most indoor and outdoor scenes and the development of a hybrid algorithm that incorporates both point and plane features provides numerous advantages. In this regard, we present a tracking algorithm for RGB-D cameras using both points and planes as primitives. We show how to e ..."
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Cited by 3 (0 self)
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Planes are dominant in most indoor and outdoor scenes and the development of a hybrid algorithm that incorporates both point and plane features provides numerous advantages. In this regard, we present a tracking algorithm for RGB-D cameras using both points and planes as primitives. We show how to extend the standard prediction-and-correction framework to include planes in addition to points. By fitting planes, we implicitly take care of the noise in the depth data that is typical in many commercially available 3D sensors. In comparison with the techniques that use only points, our tracking algorithm has fewer failure modes, and our reconstructed model is compact and more accurate. The tracking algorithm is supported by re-localization and bundle adjustment processes to demonstrate a real-time simultaneous localization and mapping (SLAM)system using a hand-held or robot-mounted RGB-D camera. Our experiments show large-scale indoor reconstruction results as point-based and plane-based 3D models, and demonstrate an improvement over the point-based tracking algorithms using a benchmark for RGB-D cameras.
Plane-based odometry using an RGB-D camera
- In British Machine Vision Conference (BMVC
, 2013
"... Odometry consists in using data from a moving sensor to estimate change in position over time. It is a crucial step for several applications in robotics and computer vision. This paper presents a novel approach for estimating the relative motion between suc-cessive RGB-D frames that uses plane-primi ..."
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Cited by 2 (0 self)
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Odometry consists in using data from a moving sensor to estimate change in position over time. It is a crucial step for several applications in robotics and computer vision. This paper presents a novel approach for estimating the relative motion between suc-cessive RGB-D frames that uses plane-primitives instead of point features. The planes in the scene are extracted and the motion estimation is cast as a plane-to-plane registra-tion problem with a closed-form solution. Point features are only extracted in the cases where the plane surface configuration is insufficient to determine motion with no ambi-guity. The initial estimate is refined in a photo-geometric optimization step that takes full advantage of the plane detection and simultaneous availability of depth and visual appearance cues. Extensive experiments show that our plane-based approach is as accu-rate as state-of-the-art point-based approaches when the camera displacement is small, and significantly outperforms them in case of wide-baseline and/or dynamic foreground. 1
Real-time large scale dense RGB-D SLAM with volumetric fusion
"... We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achi ..."
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Cited by 2 (1 self)
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We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible ” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.
