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Large Scale 3D Range Map Generation and Fusion d Kishore Pochiraju, Hao Men, Biruk Gebre

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Horizontal Configuration. Vertical Configuration. Large Scale 3D Range Map Generation and Fusion d Kishore Pochiraju, Hao Men, Biruk Gebre. 10th. 40th. Start: Red: Model Blue: Data. 1 th iteration: Red: Model White: Data. 14 th iteration: Red: Model Green: Data. 27 th iteration: - PowerPoint PPT Presentation
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Large Scale 3D Range Map Generation and Fusion d Kishore Pochiraju, Hao Men, Biruk Gebre • Carnegie Building (Design & Manufacturing Institute) 3D Scan. • Color 3D Scan Provide More Detail Information Than Range Scan. 3D Map Fusion ROAMS Specs • All Electric ATV • Power: 37 V Li-ion Battery (propulsion), 24V Lead Acid (Electronics) • Max Speed: 10 + mph • Run Time: 12 – 14 hours • Weight: 250 lb + 100 lb payload • 10% + grade climb ability • RC Joystick control (Close Range ) • Tele-operable using OCU (Long Range) Sensor Package • 1 high precision LIDAR (8/80m), 800m LIDAR option • 3 wide angle Video Cameras • 8 Front and back mounted IR proximity distance sensors • 2 wheel mounted Speed encoders • 1 steering Potentiometer • Battery Voltage sensors • GPS receiver • Low cost IMU Network • CISCO long range power injected onboard radio – 802.11g • P2P : Latency managed SMART data communications system. Iterative Closest Point Algorithm For 3D Map Fusion 1. Closest Point Association Construct k-d tree of model point cloud (3D range data). For every point in data point cloud , search the closest point in 3D space in model and associate. Color ICP search in 4D space, includes a hue vector which stands for color, provides higher accuracy and computation speed. 2. Compute Rigid Transformation Construct a matrix from associated points. Calculate Rotation & Translation Matrix from Singular Value Decomposition (SVD) of constructed matrix. 3. Error Evaluation Average distance of associated points. Examples of ICP 3D Fusion Bunny Statue 3D Reconstruction with Major Overlapping Area. Bunny Statue 3D Reconstruction with Very Limited Overlapping. Design & Manufacturing Institute Overview •Stevens Remotely Operated and Autonomous Mapping System (ROAMS) was developed for building high resolution 3D maps of Terrains and urban environments. •ROAMS Can be Remotely through the use of an OCU (Operator Control Unit) or can be operated in a semi-autonomous mode. •3D scans are acquired using a 2D Laser range scanner (LIDAR) mounted on an adaptively controlled three-degree of freedom actuator. •ROAMS could provide 3D environment scan with approximate 6DOF location information. •A variant of the Iterative Closest Point (ICP) algorithm is developed to merge 3D scan range data from different locations together, for constructing large scale 3D range maps. Horizontal Configuration Vertical Configuration ROAMS Generated 3D Maps • Stevens Lower Campus 3D Map From 9 Scan Locations. Start: Red: Model Blue: Data 27 th iteration: Red: Model Yellow: Data 1 th iteration: Red: Model White: Data 14 th iteration: Red: Model Green: Data 50 th iteration: Red: Model Green: Data 10th 20th 40th 50th 30th
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Page 1: Large Scale 3D Range Map Generation and Fusion d Kishore Pochiraju, Hao Men, Biruk Gebre

Large Scale 3D Range Map Generation and Fusiond

Kishore Pochiraju, Hao Men, Biruk Gebre

• Carnegie Building (Design & Manufacturing Institute) 3D Scan.

• Color 3D Scan Provide More Detail Information Than Range Scan.

3D Map Fusion

ROAMS Specs• All Electric ATV • Power: 37 V Li-ion Battery (propulsion), 24V Lead Acid (Electronics)• Max Speed: 10 + mph• Run Time: 12 – 14 hours• Weight: 250 lb + 100 lb payload• 10% + grade climb ability• RC Joystick control (Close Range )• Tele-operable using OCU (Long Range)

Sensor Package• 1 high precision LIDAR (8/80m), 800m LIDAR option• 3 wide angle Video Cameras• 8 Front and back mounted IR proximity distance sensors• 2 wheel mounted Speed encoders• 1 steering Potentiometer• Battery Voltage sensors• GPS receiver• Low cost IMU

Network• CISCO long range power injected onboard radio – 802.11g• P2P : Latency managed SMART data communications system.

Iterative Closest Point Algorithm For 3D Map Fusion1. Closest Point Association• Construct k-d tree of model point cloud (3D range data).• For every point in data point cloud , search the closest point

in 3D space in model and associate.• Color ICP search in 4D space, includes a hue vector which

stands for color, provides higher accuracy and computation speed.

2. Compute Rigid Transformation• Construct a matrix from associated points.• Calculate Rotation & Translation Matrix from Singular Value

Decomposition (SVD) of constructed matrix.3. Error Evaluation• Average distance of associated points.

Examples of ICP 3D Fusion• Bunny Statue 3D Reconstruction with Major Overlapping

Area.

• Bunny Statue 3D Reconstruction with Very Limited Overlapping.

Design & Manufacturing Institute

Overview•Stevens Remotely Operated and Autonomous Mapping System (ROAMS) was developed for building high resolution 3D maps of Terrains and urban environments.•ROAMS Can be Remotely through the use of an OCU (Operator Control Unit) or can be operated in a semi-autonomous mode.•3D scans are acquired using a 2D Laser range scanner (LIDAR) mounted on an adaptively controlled three-degree of freedom actuator.•ROAMS could provide 3D environment scan with approximate 6DOF location information.•A variant of the Iterative Closest Point (ICP) algorithm is developed to merge 3D scan range data from different locations together, for constructing large scale 3D range maps.

Horizontal Configuration Vertical Configuration

ROAMS Generated 3D Maps• Stevens Lower Campus 3D Map From 9 Scan Locations.

Start:Red: ModelBlue: Data

27th iteration:Red: ModelYellow: Data

1th iteration:Red: ModelWhite: Data

14th iteration:Red: ModelGreen: Data

50th iteration:Red: ModelGreen: Data

10th 20th

40th 50th30th

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