1. Paper Presentation Aryeh Zapinsky
Transformable Semantic Map Based Navigation using Autonomous Deep Learning Object Segmentation
Yuki Furuta, Kentaro Wada, Masaki Murooka, Shunichi Nozawa, Yohei Kakiuchi, Kei Okada and Masayuki Inaba

2. [1]

3. Overview of System Autonomous dataset generation Semantic map creation
Dynamic environment
Robot invariance
[2]

4. Previous Work: Mapping
Raw Spatial Data
(Fox et al.)
Semantic Map +
Annotated World
Abstracted Spatial Mapping
(Kuipers)
Can localize self with high precision.
Can handle dynamic change of environment.

5. Previous Work: Object Segmentation
Long et al. proposed fully convolutional network enables end-to-end learning for object segmentation. [3]

6. Generating Dataset Datasets are environment specific
Autonomous image generation
Project bounding box → Take point cloud within box → Project masked point cloud back to image → label masked images
Reduce image input
[2]

7. Object Segmentation
5 object classes (as opposed to 20 from previous experiment)
Mislabeled images
[2]
[3]

8. Object Segmentation (cont.)
Use gradient clipping strategy to minimize effect of false label
[2]

9. Object Segmentation (cont.)
418 sets of RGB and Label images
8:2 training to validation
Segmentation accuracy increases with increased training iterations
[2]

10. Navigation based on Segmentation
Registration of FCN segmentation to 3D point cloud
Denoise filter by clustering
Box fitting to estimate size of detected object
Center and size for localization
Thresholding for detection
[2]

11. Experiments Acquisition Phase Task Execution Phase
250 minutes - capturing 450,973 color images and 445,611 depth images
Result: 418* annotated images
Task Execution Phase
Robot invariant - same framework with change in robot (HRP2)
Room invariant - PR2 with change in room layout
[1]
* The paper at one point lists 418 and at another point 417. For consistency within the slides, I have used 418 as the number of image sets.

12. Task Compiler Combines environmental states and goal states.
Action sequence + Failure states
[2]

13. Task Execution (PR2)
[2]

14. Task Execution (PR2 & HRP2)
[2]

15. Task Execution (PR2 & HRP2)
[2]

16. Change in Room Layout
Using semantic mapping for room navigation
[2]

17. System in Action
[2]

18. Paper’s Conclusion
Novel framework for dealing with dynamic environment
Demonstrated feasibility of performance in home environment
Robotic task independent of robot type

19. Comments Annotated world Generalization of feasibility of approach
Use of
Seeming reliance for initial actions
Generalization of feasibility of approach
5 vs. 20 object classes
Poor written English obscured some ideas

20. References and Resources
[1]
[2]
[3]