Confined Space Mapping Module for In-Pipe Repair Robots

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


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.



Submillimeter

accuracy



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



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


Summary Slide


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.



Submillimeter

accuracy



1 inch

5 meters

Howie

Choset

PI

Thanks for listeningThis project is funded by ARPA-E REPAIR

Program

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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

Documents

Confined Space Mapping Module for In-Pipe Repair Robots