Abstract: Examples disclosed herein may involve a computing system that is operable to (i) identify a set of vehicle trajectories that are associated with a segment of a road network, (ii) identify a first cluster of sampling points between the identified set of vehicle trajectories and a first sampling position along the segment, wherein the first cluster has an associated geospatial position and is inferred to be associated with one given lane of the segment, (iii) identify a subset of vehicle trajectories in the identified set that are inferred to be associated with the given lane between the first sampling position and a second sampling position along the segment, (iv) identify a second cluster of sampling points between the identified subset of vehicle trajectories and the second sampling position, wherein the second cluster has an associated geospatial position, and (v) determine a geospatial geometry of the given lane.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) receive image data and corresponding secondary sensor data, (ii) generate a reconstruction of a map from the image data, wherein the reconstruction comprises sequential pose information, (iii) determine constraints from the secondary sensor data, and (iv) validate the reconstruction of the map by applying the determined constraints from the secondary sensor data to the determined sequential pose information from the reconstruction of the map and determining whether the sequential pose information fails to satisfy any of the constraints determined from the secondary sensor data.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) identify a set of vehicle trajectories that are associated with a segment of a road network, (ii) identify a first cluster of sampling points between the identified set of vehicle trajectories and a first sampling position along the segment, wherein the first cluster has an associated geospatial position and is inferred to be associated with one given lane of the segment, (iii) identify a subset of vehicle trajectories in the identified set that are inferred to be associated with the given lane between the first sampling position and a second sampling position along the segment, (iv) identify a second cluster of sampling points between the identified subset of vehicle trajectories and the second sampling position, wherein the second cluster has an associated geospatial position, and (v) determine a geospatial geometry of the given lane.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) receive image data and corresponding secondary sensor data, (ii) generate a reconstruction of a map from the image data, wherein the reconstruction comprises sequential pose information, (iii) determine constraints from the secondary sensor data, and (iv) validate the reconstruction of the map by applying the determined constraints from the secondary sensor data to the determined sequential pose information from the reconstruction of the map and determining whether the sequential pose information fails to satisfy any of the constraints determined from the secondary sensor data.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) receive image data and corresponding secondary sensor data, (ii) generate a reconstruction of a map from the image data, wherein the reconstruction comprises sequential pose information, (iii) determine constraints from the secondary sensor data, and (iv) validate the reconstruction of the map by applying the determined constraints from the secondary sensor data to the determined sequential pose information from the reconstruction of the map and determining whether the sequential pose information fails to satisfy any of the constraints determined from the secondary sensor data.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) receive image data and corresponding secondary sensor data, (ii) generate a reconstruction of a map from the image data, wherein the reconstruction comprises sequential pose information, (iii) determine constraints from the secondary sensor data, and (iv) validate the reconstruction of the map by applying the determined constraints from the secondary sensor data to the determined sequential pose information from the reconstruction of the map and determining whether the sequential pose information fails to satisfy any of the constraints determined from the secondary sensor data.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) identify a set of vehicle trajectories that are associated with a segment of a road network, (ii) identify a first cluster of sampling points between the identified set of vehicle trajectories and a first sampling position along the segment, wherein the first cluster has an associated geospatial position and is inferred to be associated with one given lane of the segment, (iii) identify a subset of vehicle trajectories in the identified set that are inferred to be associated with the given lane between the first sampling position and a second sampling position along the segment, (iv) identify a second cluster of sampling points between the identified subset of vehicle trajectories and the second sampling position, wherein the second cluster has an associated geospatial position, and (v) determine a geospatial geometry of the given lane.

Abstract: The present invention relates to a method for evaluating map quality in relation to the suitability of the map for visual localization. More particularly, the present invention relates to a method for providing feedback for identifying where maps need improvement and/or deciding when a map is of sufficient quality to perform localization.