Wide-View LIDAR With Areas of Special Attention
US-2016274589-A1 · Sep 22, 2016 · US
US11961208B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11961208-B2 |
| Application number | US-202217578750-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jan 19, 2022 |
| Priority date | Jul 31, 2017 |
| Publication date | Apr 16, 2024 |
| Grant date | Apr 16, 2024 |
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
What the patent document calls the invention.
A short plain-language summary of the technical disclosure.
Who owns or filed the patent and who is credited as inventor.
Filing, priority, publication, and grant dates set the timeline.
The legal scope of protection — read this for what is actually claimed.
Technology tags used to group this patent with similar filings.
Prior art links and similar publications in this corpus.
Official abstract text for this publication.
Correcting motion-based inaccuracy in point cloud data generated by one or more sensors carried by a scanning platform, and associated systems and methods are disclosed herein. A representative method includes associating a motion model with a target object of the point cloud, estimating adjusting factors based on the motion model, and adjusting scanning points in the point cloud using the adjusting factors.
Opening claim text (preview).
We claim: 1. A computer-implemented method for adjusting point clouds generated using at least one scanner carried by a scanning platform, the method comprising: obtaining base point cloud information comprising a plurality of scanning points that are produced by the at least one scanner, wherein at least one subset of the scanning points indicates a position of at least a portion of a target object; determining an estimated motion of the target object based, at least in part, on a volume defined by the plurality of scanning points; relocating at least one scanning point of the plurality of scanning points based, at least in part, on the estimated motion of the target object in accordance with a motion model associated with the target object; and generating an adjusted point cloud based, at least in part, on the relocating of the at least one scanning point, wherein relocating the at least one scanning point is based, at least in part, on movements associated with the at least one scanning point between a timepoint when the at least one scanning point was produced and a subsequent target timepoint. 2. The method of claim 1 , further comprising determining a maximum time period for generating the adjusted point cloud based on an error distance of the adjusted point cloud. 3. The method of claim 2 , wherein the volume defined in accordance with the plurality of scanning points includes a minimized quantity of volume pixels occupied by the plurality of scanning points at a target timepoint, in accordance with the motion model associated with the target object. 4. The method of claim 2 , wherein the volume defined in accordance with the plurality of scanning points includes a minimized volume enclosed by the plurality of scanning points at a target timepoint, in accordance with the motion model associated with the target object. 5. The method of claim 1 , wherein a relative distance between the target object and the scanning platform changes during a period of time. 6. The method of claim 1 , wherein the adjusted point cloud represents at least one of a location, orientation, or shape of the target object at the end of a period of time. 7. The method of claim 1 , wherein the estimated motion of the target object includes at least one of a translational motion or a rotational motion. 8. The method of claim 1 , further comprising locating the target object based, at least in part, on the adjusted point cloud. 9. A non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause one or more processors associated with a scanning platform to perform actions, the actions comprising: obtaining base point cloud information comprising a plurality of scanning points that are produced by at least one scanner carried by the scanning platform, wherein at least one subset of the scanning points indicates a position of at least a portion of a target object; determining an estimated motion of the target object based, at least in part, on a volume defined by the plurality of scanning points; relocating at least one scanning point of the plurality of scanning points based, at least in part, on the estimated motion of the target object in accordance with a motion model associated with the target object; and generating an adjusted point cloud based, at least in part, on the relocating of the at least one scanning point, wherein relocating the at least one scanning point is based, at least in part, on movements associated with the at least one scanning point between a timepoint when the at least one scanning point was produced and a subsequent target timepoint. 10. The computer-readable medium of claim 9 , wherein the actions further comprise locating the target object based, at least in part, on the adjusted point cloud. 11. The computer-readable medium of claim 9 , wherein the scanning points are represented within a three-dimensional reference system associated with the at least one scanner or the scanning platform. 12. The computer-readable medium of claim 9 , wherein the actions further comprise estimating the motion of the target object based, at least in part, on the motion model that includes at least one of a translational motion component or a rotational motion component. 13. The computer-readable medium of claim 12 , wherein the translational motion component includes at least one of a constant translational speed factor or a variable translational speed factor. 14. The computer-readable medium of claim 12 , wherein the rotational motion component includes at least one of a constant rotational speed factor or a variable rotational speed factor. 15. A vehicle including a controller programmed to at least partially control one or more motions of the vehicle, wherein the programmed controller includes one or more processors configured to: obtain base point cloud information comprising a plurality of scanning points that are produced by at least one scanner, wherein at least one subset of the scanning points indicates a position of at least a portion of a target object; determine an estimated motion of the target object based, at least in part, on a volume defined by the plurality of scanning points; relocate at least one scanning point of the plurality of scanning points based, at least in part, on the estimated motion of the target object in accordance with a motion model associated with the target object; and generate an adjusted point cloud based, at least in part, on the relocating of the at least one scanning point, wherein relocating the at least one scanning point is based, at least in part, on movements associated with the at least one scanning point between a timepoint when the at least one scanning point was produced and a subsequent target timepoint. 16. The vehicle of claim 15 , wherein the one or more processors are further configured to determine a maximum time period for generating the adjusted point cloud based on an error distance of the adjusted point cloud. 17. The vehicle of claim 15 , wherein the adjusted point cloud represents at least one of a location, orientation, or shape of the target object at the end of a period of time. 18. The vehicle of claim 15 , wherein the vehicle includes at least one of an unmanned aerial vehicle (UAV), a manned aircraft, an autonomous car, a self-balancing vehicle, a robot, a smart wearable device, a virtual reality (VR) head-mounted display, or an augmented reality (AR) head-mounted display.
Evaluating distance, position or velocity data · CPC title
Physics · mapped topic
using two or more images, e.g. averaging or subtraction · CPC title
involving models · CPC title
exterior to a vehicle by using sensors mounted on the vehicle · CPC title
Related publications grouped by family.
Answers are generated from the same data shown on this page.