1 / 1

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

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:

milton
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Horizontal Configuration Vertical Configuration Large Scale 3D Range Map Generation and FusiondKishore Pochiraju, Hao Men, Biruk Gebre 10th 40th Start: Red: Model Blue: Data 1th iteration: Red: Model White: Data 14th iteration: Red: Model Green: Data 27th iteration: Red: Model Yellow: Data 50th iteration: Red: Model Green: Data 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. 20th 50th • Carnegie Building (Design & Manufacturing Institute) 3D Scan. • Color 3D Scan Provide More Detail Information Than Range Scan. 3D Map Fusion • Iterative Closest Point Algorithm For 3D Map Fusion • 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. • Compute Rigid Transformation • Construct a matrix from associated points. • Calculate Rotation & Translation Matrix from Singular Value Decomposition (SVD) of constructed matrix. • 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. • 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. 30th • ROAMS Generated 3D Maps • Stevens Lower Campus 3D Map From 9 Scan Locations.

More Related