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EECSCon Poster
1. Point Cloud
Extraction
Camera Pose
Estimation
Place Recognition
Cloud Slice
Extraction
World Model
Update
TSDF
Pose Graph
DBoW
Database
3D Depth
Point Cloud
R
G
B-D
Loop Closure
Constraint
Pose
Odometry
Pose
RGB-D
Pose
Data Collection Module
Point Cloud Data
(Cloud Slices,
TSDF) + Pose
Graph
Pose
Optimization
Poses
Optimized
Map
Post Processing Module
3D Point
Cloud
Legend
Data Move
Conditional
Data Move
Data Store
Module
Component
Deformation
Deformation
Graph
RGB-D Frame
TSD
F
C
loud
Slices
Poses
RGB-D Frame [8] 3D Depth Point Cloud [4] Pose Graph
Truncated Signed Distance
Function (TSDF) [5] Cloud Slice Extraction [5]
Simultaneous Localization and Mapping (SLAM)
Vincent Kee1, Matthew Graham2, Jeffrey Shapiro1
1Department of Electrical Engineering and Computer Science, MIT 2Machine Intelligence Group, Charles Stark Draper Laboratory
Robust Large Scale Reconstruction of
Indoor Scenes
Main Contribution: Deformation Graph (D-Graph) [7] Evaluation Metrics
Current Capabilities:
Accurate model on small-scale or trajectory on large-scale
References
[1] http://www.google.com/selfdrivingcar/images/home-where.jpg
[2] http://www.darpa.mil/DDM_Gallery/FLAMissionGraphicMedim%20619x316.jpg
[3] http://www.augmentedrealitytrends.com/wp-content/uploads/2014/08/ARC4-augmented-reality-system.jpg
[4] Newcombe, et al. KinectFusion: Real-time dense surface mapping and tracking (2011)
[5] Whelan, et al. Real-time Large Scale Dense RGB-D SLAM with Volumetric Fusion. (2012)
[6] Whelan, et al. Elasticfusion: Dense slam without a pose graph. (2015)
[7] Sumner, et al. Embedded deformation for shape manipulation. (2007)
[8] Handa, et al. A benchmark for RGB-D visual odometry, 3D reconstruction, and SLAM (2014)
(1) Surface model
(2) D-Graph rep
(3) Deformed D-Graph
(4) Reconstructed
surface model
2
3
1
4
System Block Diagram
Motivation:
Comparison of Dense Visual SLAM Algorithms
KinectFusion [4] Kintinuous [5] ElasticFusion [6]
Previous Dense Visual SLAM Techniques
x2
x1
x3
x4
x5
Synthetic Datasets Real World Datasets
Trajectory Root Mean Square
Error (RMSE)
Surface Model Error
Ground Truth Model [8]
Trajectory RMSE
Motion tracking for truth
Estimated vs. Truth Trajectory [5]
1 6
5
3
4
2
Accurate
Model
Model
Resolution
Accurate
Trajectory
Estimation
(1) SuperUROP
(2) Draper’s System
(3) Kintinuous
(4) ElasticFusion
(5) KinectFusion
(6) SLAM SystemsSelf-Driving Cars [1] Autonomous Quadrotors [2] Augmented Reality [3]
![Point Cloud
Extraction
Camera Pose
Estimation
Place Recognition
Cloud Slice
Extraction
World Model
Update
TSDF
Pose Graph
DBoW
Database
3D Depth
Point Cloud
R
G
B-D
Loop Closure
Constraint
Pose
Odometry
Pose
RGB-D
Pose
Data Collection Module
Point Cloud Data
(Cloud Slices,
TSDF) + Pose
Graph
Pose
Optimization
Poses
Optimized
Map
Post Processing Module
3D Point
Cloud
Legend
Data Move
Conditional
Data Move
Data Store
Module
Component
Deformation
Deformation
Graph
RGB-D Frame
TSD
F
C
loud
Slices
Poses
RGB-D Frame [8] 3D Depth Point Cloud [4] Pose Graph
Truncated Signed Distance
Function (TSDF) [5] Cloud Slice Extraction [5]
Simultaneous Localization and Mapping (SLAM)
Vincent Kee1, Matthew Graham2, Jeffrey Shapiro1
1Department of Electrical Engineering and Computer Science, MIT 2Machine Intelligence Group, Charles Stark Draper Laboratory
Robust Large Scale Reconstruction of
Indoor Scenes
Main Contribution: Deformation Graph (D-Graph) [7] Evaluation Metrics
Current Capabilities:
Accurate model on small-scale or trajectory on large-scale
References
[1] http://www.google.com/selfdrivingcar/images/home-where.jpg
[2] http://www.darpa.mil/DDM_Gallery/FLAMissionGraphicMedim%20619x316.jpg
[3] http://www.augmentedrealitytrends.com/wp-content/uploads/2014/08/ARC4-augmented-reality-system.jpg
[4] Newcombe, et al. KinectFusion: Real-time dense surface mapping and tracking (2011)
[5] Whelan, et al. Real-time Large Scale Dense RGB-D SLAM with Volumetric Fusion. (2012)
[6] Whelan, et al. Elasticfusion: Dense slam without a pose graph. (2015)
[7] Sumner, et al. Embedded deformation for shape manipulation. (2007)
[8] Handa, et al. A benchmark for RGB-D visual odometry, 3D reconstruction, and SLAM (2014)
(1) Surface model
(2) D-Graph rep
(3) Deformed D-Graph
(4) Reconstructed
surface model
2
3
1
4
System Block Diagram
Motivation:
Comparison of Dense Visual SLAM Algorithms
KinectFusion [4] Kintinuous [5] ElasticFusion [6]
Previous Dense Visual SLAM Techniques
x2
x1
x3
x4
x5
Synthetic Datasets Real World Datasets
Trajectory Root Mean Square
Error (RMSE)
Surface Model Error
Ground Truth Model [8]
Trajectory RMSE
Motion tracking for truth
Estimated vs. Truth Trajectory [5]
1 6
5
3
4
2
Accurate
Model
Model
Resolution
Accurate
Trajectory
Estimation
(1) SuperUROP
(2) Draper’s System
(3) Kintinuous
(4) ElasticFusion
(5) KinectFusion
(6) SLAM SystemsSelf-Driving Cars [1] Autonomous Quadrotors [2] Augmented Reality [3]](https://image.slidesharecdn.com/ab0bab2c-a519-4979-8b6e-08c69fda9188-160713013417/85/eecscon-poster-1-320.jpg)
