Patch Volumes: Segmentation-Based Consistent Mapping with RGB-D Cameras

Henry, Peter; Fox, Dieter; Bhowmik, Achintya K.; Mongia, Rajiv

doi:10.1109/3dv.2013.59

Cited by 71 publications

(68 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We adopt an architecture which is typically found in realtime dense visual SLAM systems that alternates between tracking and mapping [15,25,9,8,2,16]. Like many dense SLAM systems ours makes significant use of GPU programming.…”

Section: Approach Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Robotics: Science and Systems XI

2015

View full text Add to dashboard Cite

Abstract-We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any postprocessing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimisations as often as possible to stay close to the mode of the map distribution, while utilising global loop closure to recover from arbitrary drift and maintain global consistency.

show abstract

Section: Approach Overviewmentioning

confidence: 99%

“…with w rgb = 0.1 in line with related work [8,25]. For this we use the Gauss-Newton non-linear least-squares method with a three level coarse-to-fine pyramid scheme.…”

Section: Joint Optimisationmentioning

confidence: 99%

Robotics: Science and Systems XI

2015

View full text Add to dashboard Cite

show abstract

“…Recently introduced depth cameras along with highly parallel algorithms optimized for modern GPUs have enabled new algorithms for tracking complex 3D objects in real time. Examples include KinectFusion and related efforts for 3D mapping [23,16,34], human body pose tracking [29,35,15], articulated hand tracking [24,19,26]. These approaches were developed for specific application domains and have not been demonstrated or tested on multiple tracking applications.…”

Section: Introductionmentioning

confidence: 99%

“…DART represents objects via an articulated version of the signed distance function (SDF), which has been used to achieve very robust and efficient results for online 3D mapping [23,16,34] and for tracking rigid objects in six degrees of freedom [10,27,22,3,5]. Our tracking framework uses a local gradient based approach to find the pose of the model which best explains the data points observed in a depth frame along with a prior based on previous data.…”

Section: Introductionmentioning

confidence: 99%

DART: Dense Articulated Real-Time Tracking

Schmidt

Newcombe

Fox

2014

Robotics: Science and Systems X

Self Cite

136

130

View full text Add to dashboard Cite

Abstract-This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies connected through a kinematic tree. DART covers a broad set of objects encountered in indoor environments, including furniture and tools, and human and robot bodies, hands and manipulators. To achieve efficient and robust tracking, DART extends the signed distance function representation to articulated objects and takes full advantage of highly parallel GPU algorithms for data association and pose optimization. We demonstrate the capabilities of DART on different types of objects that have each required dedicated tracking techniques in the past. I. INTRODUCTIONThe ability to accurately track the pose of objects in real time is of fundamental importance to many areas of robotics. Applications range from navigation to planning, manipulation and human-robot interaction, all of which have received the attention of researchers working within a state-space modelbased paradigm within both computer vision and robotics. The class of objects that can be described as collections of rigid bodies chained together through a kinematic tree is quite broad, including furniture, tools, human bodies, human hands, and robot manipulators. Tracking articulated bodies from a single viewpoint and without instrumenting the object of interest still presents a significant challenge where the single viewpoint and occlusions, including self-occlusion, limit the amount of information available for pose estimation. Noisy sensor data and approximate object models pose additional problems. Finally, the objects being tracked can be highly dynamic and have many degrees of freedom, making real-time tracking difficult.Early articulated model-based tracking techniques relied on tracking 2D features such as image edges on a CPU [8,4]. Recently introduced depth cameras along with highly parallel algorithms optimized for modern GPUs have enabled new algorithms for tracking complex 3D objects in real time. Examples include KinectFusion and related efforts for 3D mapping [23,16,34], human body pose tracking [29,35,15], articulated hand tracking [24,19,26]. These approaches were developed for specific application domains and have not been demonstrated or tested on multiple tracking applications. The application-specific nature of these approaches enables their authors to show excellent performance by taking advantage of domain-specific features and constraints, but it also prevents them from serving as general tools for tracking arbitrary articulated objects. Techniques have also been developed to

show abstract

“…The segmentation is followed by convex hull detection to extract boundary and non-boundary points. This representation offers a more semantic representation to the point cloud data (Henry et al, 2013). The flowchart of the algorithm is illustrated in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

RGB-D Indoor Plane-based 3D-Modeling using Autonomous Robot

Mostofi

Moussa

Elhabiby

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

3D model of indoor environments provide rich information that can facilitate the disambiguation of different places and increases the familiarization process to any indoor environment for the remote users. In this research work, we describe a system for visual odometry and 3D modeling using information from RGB-D sensor (Camera). The visual odometry method estimates the relative pose of the consecutive RGB-D frames through feature extraction and matching techniques. The pose estimated by visual odometry algorithm is then refined with iterative closest point (ICP) method. The switching technique between ICP and visual odometry in case of no visible features suppresses inconsistency in the final developed map. Finally, we add the loop closure to remove the deviation between first and last frames. In order to have a semantic meaning out of 3D models, the planar patches are segmented from RGB-D point clouds data using region growing technique followed by convex hull method to assign boundaries to the extracted patches. In order to build a final semantic 3D model, the segmented patches are merged using relative pose information obtained from the first step.

show abstract

Patch Volumes: Segmentation-Based Consistent Mapping with RGB-D Cameras

Cited by 71 publications

References 12 publications

Robotics: Science and Systems XI

Robotics: Science and Systems XI

DART: Dense Articulated Real-Time Tracking

RGB-D Indoor Plane-based 3D-Modeling using Autonomous Robot

Contact Info

Product

Resources

About