US 12,493,120 B2
Laser scanner with real-time, online ego-motion estimation
Ji Zhang, Pittsburgh, PA (US); Sanjiv Singh, Pittsburgh, PA (US); and Kevin Joseph Dowling, Gibsonia, PA (US)
Filed by CARNEGIE MELLON UNIVERSITY, Pittsburgh, PA (US)
Filed on Aug. 12, 2022, as Appl. No. 17/886,692.
Application 17/886,692 is a continuation of application No. 16/380,088, filed on Apr. 10, 2019, granted, now 11,567,201.
Application 16/380,088 is a continuation of application No. PCT/US2017/055938, filed on Oct. 10, 2017.
Application PCT/US2017/055938 is a continuation in part of application No. PCT/US2017/021120, filed on Mar. 7, 2017.
Application 16/380,088 is a continuation in part of application No. 16/125,054, filed on Sep. 7, 2018, granted, now 10,962,370, issued on Mar. 30, 2021.
Application 16/125,054 is a continuation of application No. PCT/US2017/021120, filed on Mar. 7, 2017.
Claims priority of provisional application 62/406,910, filed on Oct. 11, 2016.
Claims priority of provisional application 62/307,061, filed on Mar. 11, 2016.
Prior Publication US 2023/0130320 A1, Apr. 27, 2023
Int. Cl. G01S 17/42 (2006.01); G01S 7/48 (2006.01); G01S 7/51 (2006.01); G01S 17/66 (2006.01)
CPC G01S 17/42 (2013.01) [G01S 7/4808 (2013.01); G01S 7/51 (2013.01); G01S 17/66 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method of operating a simultaneous location and mapping (SLAM) system comprising:
acquiring a point cloud from a LIDAR, the point cloud comprising a plurality of points each of which are attributed with at least a geospatial coordinate and a timestamp;
using the SLAM system:
determining a confidence metric of the plurality of points;
displaying at least a portion of the plurality of points;
displaying, to a user, an indication of a portion of the point cloud exhibiting the confidence metric below a predetermined threshold,
wherein displaying the indication of a portion of the point cloud exhibiting a confidence metric below the predetermined threshold comprises displaying a target location for resuming scanning; and
resolving discrepancies between points in the point cloud by preferentially adjusting location estimates for a segment of the plurality of the points, wherein resolving discrepancies comprises reacquiring a portion of the plurality of points associated with the lower confidence metric, applying a correction to a position of a point when an end point differs from a known origin location for a closed loop point cloud, iteratively refining a residual error between the point cloud and a second point cloud having a geospatial coordinate near the geospatial coordinate of the point cloud, or fusing the point cloud with other content synchronized with the timestamp.