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Confined Space Mapping Module
for In-Pipe Repair Robots
Howie Choset, CMU
Cutting-edge plug-and-play mapping technology for any conventional in-
pipe mobility platform.
Total Project Cost: $1.2M
Length 36 mo.
Project Vision
The Concept
‣ Enabling technology for Simultaneous Localization And Mapping (SLAM) in Pipeline➢Infrastructure-free➢Ultra-large scale & hi-res➢Compact hardware➢Easy to integrate➢Low-cost (relatively)
‣ State-of-the-art vs ARPA-E Unit:
In-Pipe SLAM Module & Ultra-Large Scale SubT Mapping
Compact Lidar design for confined space inspection
Submillimeter
accuracy
© CMU Biorobotics Lab
© CMU Biorobotics Lab
1 inch
5 meters
Metric State of the Art Proposed
Point Cloud Map Size <50 Million Pts 500 Million Pts
Min. Scannable Pipe ID ≥20-inch <10-inch
SLAM Sensing Modality Lidar-based Multimodal
Cost of Integration High and time consuming Plug-n-Play
Cost Per System Not exist for small pipes $20,000
Summary Slide
2Insert Presentation NameNovember 18, 2020
Technology Summary
- Robust SLAM in a reduced feature environments.
- Multimodal sensor fusion and feature registration.
- Ultra-large scale map manipulation and processing
- Compact Lidar and In-Pipe SLAM Module Design.
Impacts
- 10x map density, sum-mm accuracy, works in small pipes.
- Modular / Multimodal approach maximizes upgrability.
- Near-zero integration time and R&D cost for users.
- AI-enabled force multiplier for field technicians.
In-Pipe SLAM Module & Ultra-Large Scale SubT Mapping
Compact Lidar design for confined space inspection
Submillimeter
accuracy
© CMU Biorobotics Lab
© CMU Biorobotics Lab
1 inch
5 meters
Cutting-edge plug-and-play mapping
technology for any conventional in-pipe mobility platform.
Howie
Choset
PI
The Team
3
Howie
Choset
PI
Michael
Schwerin
Research
Programmer
Charlie
Hart
System
Engineer
Nicholas
Paiva
Electrical
Engineer
Daqian
Cheng
MS StudentMapping SW
Matthew
Travers
Co-PI (Mapping)
Lu
Li
Co-PI (Sensing)
Biorobotics
Lab
Project Objectives
4
Robotics
Research
Robust mapping
in a feature-free
and feature
sparse
environments
Enabling
Technology
To develop a robotic
mapping sensor
hardware and
software tool for the
pipeline industry
Cost/barrier
Reduction
Rapidly design &
deploy the next-gen
mapping-enabled
machinery & service,
with minimal cost
Force
Multiplier
Intuitive to use UI,
real-time map
visualization, with AI-
enabled data
processing
❖Easy-to-integrate mapping sensor module
❖In-Pipe mapping and sensor fusion software
5
❖Easy-to-integrate mapping sensor module
❖In-Pipe mapping and sensor fusion software
Deliverables
➢ Deliverable 1: Mapping and multimodal data fusion software
framework and full system demonstration for pipe feature
registration and large-scale mapping (3D RGB-Depth).
➢ Deliverable 2: Full function prototype and testing inside both
mock-up and a real gas pipe.
➢ Deliverable 3: Post-processing and visualization user
interface demonstration. (Real-time 3D map generation)
➢ Deliverable 4: Deployment and demonstrate system
capability inside gas pipe network.
Challenges and Solutions
6
Challenges in Pipeline mapping and inspection, in a roboticist perspective
Lack of Features
Repeating Patterns
TightSpaces
Challenging Environments
RobustState
Estimation
MultimodalSensorFusion
Ultra-short Range
Perception
RuggedSystemDesign
Method and Approach
Key Approaches‣ Reduced feature mapping and localization inside a pipe‣ Multimodal information fusion for registration‣ Hybrid sensing for hybrid mapping‣ Compact in-pipe inspection sensor design‣ Large-scale underground pipe network mapping
Core technologies/competencies of the team
7
©CMU Biorobotics Lab
©CMU Biorobotics Lab
©CMU Biorobotics Lab
Proprietary Sensor Design Sparse-Feature Fast Registration Ultra-Large-Scale SLAM
Project Tasks
‣ Task 1: Confined Space Localization and Mapping– Task 1.1 SLAM framework for confined space
– Task 1.2 Multimodal data fusion, registration, and optimization
‣ Task 2: Novel Sensor Design for Visual and Geometry Inspection– Task 2.1 Ultra-short range sensor custom design
– Task 2.2 Software for in-pipe visual and geometry inspection
– Task 2.3 In-Pipe SLAM Module design and fabrication
‣ Task 3: Intelligent Data Post-processing and visualization– Task 3.1 Ultra-large-scale point cloud registration and visualization
– Task 3.2 AI-powered data post-processing
‣ Task 4. Integration– Task 4.1 SLAM module integration with CMU confined space robot.
– Task 4.2* SLAM module integration with other REPAIR teams
9
Performance Metrics
All-in-one solution for ~12-inch pipe mapping & inspection
Disruptive performance-to-cost ratio
10
Sensor TypeSLAM
Method(s)Map Scale
Geometric
Accuracy
Color
CaptureFrequency Drift Rate
Sensing
Range
Camera-BasedORB-SLAM,
DSOMedium cm-grade Yes 30 fps 2%
≥ 0.1m,
< 20m
RGB-D CameraKinectFusion,
ElasticFusion
Small
(indoor)mm-grade Yes 30 fps 1%
≥ 0.2m,
< 10m
LiDAR-Based LOAM Large mm-grade No 5 - 10 fps 0.5%≥ 1m,
< 100m
Proposed Multimodal System* Large mm-grade Yes ~ 60 fps 0.5%≥ 0.02m,
< 100m
* Target specs of the proposed systemGreen - Good Red - Bad
Project Timeline and Milestones
11
T2M/Commercialization
‣ Start up– Prof. Choset graduated from UPenn Entrepreneurial
Management and had already start 4 companies: medical, modular robots, Logistic, and ventilators.
– CMU has very supportive and generous gesture toward faculty staff and student to start companies
– Pittsburgh have both the infrastructure and ecosystem to support tech start-ups.
‣ Licensing– We are open to talking to other partners to have exclusive or
non-exclusive licensing options.
‣ Part of a bigger system (Start up)
12
Potential Partnerships
13Insert Presentation NameNovember 18, 2020
Summary Slide
14Insert Presentation NameNovember 18, 2020
Technology Summary
- Robust SLAM in a reduced feature environments.
- Multimodal sensor fusion and feature registration.
- Ultra-large scale map manipulation and processing
- Compact Lidar and In-Pipe SLAM Module Design.
Impacts
- 10x map density, sum-mm accuracy, works in small pipes.
- Modular / Multimodal approach maximizes upgrability.
- Near-zero integration time and R&D cost for users.
- AI-enabled force multiplier for field technicians.
In-Pipe SLAM Module & Ultra-Large Scale SubT Mapping
Compact Lidar design for confined space inspection
Submillimeter
accuracy
© CMU Biorobotics Lab
© CMU Biorobotics Lab
1 inch
5 meters
Howie
Choset
PI
Thanks for listeningThis project is funded by ARPA-E REPAIR
Program
Q & A • ATTENDEES: Use the Q&A panel:
• Open the Q&A panel if it is not already open by clicking the More Options button (next to the “Chat” button in your attendee controls) and selecting Q&A.
• Type your question in the box, select “All Panelists” in the Ask drop-down list, and click Send.
• The Q&A Moderator will ask your question verbally, and the appropriate Speaker will respond. If additional clarification is needed, the Moderator will ask the Host to unmute you so you can clarify your question.
• Panelists may not submit questions via the Q&A panel but may respond to questions in the panel if appropriate.
• PANELISTS/SPEAKERS: Use the “Raise Hand” tool • Click the Hand icon in at the bottom-right corner of the Participant list. • The MC will acknowledge your raised hand and ask the Host to unmute your mic
so you can ask your question. • Click the “Lower Hand” option (same button as earlier) to “lower” your hand. • Panelists/Speakers MUST use the “Raise Hand” tool to ask a question.
15
CLOSING REMARKSWITH Q&A
16Insert Presentation NameJanuary 11, 2021
Jack Lewnard
Program Director, ARPA-E
HAPPY HOUR/NETWORKING (OPTIONAL)‣ Thank you for your participation!
‣ Program Director, Jack Lewnard will be available for additional open discussion
‣ Contact information is shared if you have given us permission
17Insert Presentation NameJanuary 11, 2021
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