Abstract: Embodiments of the present disclosure relate to a system and method used to localize one or more systems using 2D map data. The method may include determining an image location of a representation of a portion of an object in an image corresponding to an environment. In some embodiments, the method may additionally include determining one or more predicted image locations corresponding to the image location of the representation of the portion of the object. The method may additionally include comparing one or more ground plane locations of the portion of the object with the one or more predicted image locations, and determining a cost based at least on the comparison between the one or more ground plane locations and the one or more predicted image locations. Further, the method may include localizing a system to the 2D map data based on the determined cost.

Abstract: In various examples, accuracy determinations for localization in autonomous and semi-autonomous systems and applications are described herein. Systems and methods are disclosed that determine one or more errors associated with vehicle localization using various types of sensor data generated using a vehicle. For instance, a first component of the vehicle may use a map and first sensor data to determine an estimated pose of the vehicle. A second component of the vehicle may then determine the error(s) associated with the estimated pose based on both actual motion of the vehicle within the environment, as determined using second sensor data, and comparing features represented by the first sensor data to features represented by the map. In some examples, the second component may further determine information associated with the error(s), such as one or more uncertainties associated with the error(s).

Abstract: One variation of a method for updating a localization map for a fleet of autonomous vehicles includes: selecting a set of roads including a first subset of road segments associated with existing incomplete scan data and a second subset of road segments associated with a monitoring request from an external entity; during a passenger period at the autonomous vehicle, autonomously transporting passengers according to a series of ride requests; during a mapping period succeeding the passenger period at the autonomous vehicle, autonomously navigating along the set of road segments, recording a first series of scan data representing surfaces proximal the first subset of road segments, and recording a second series of scan data representing surfaces proximal the second subset of road segments; updating the localization map based on the first series of scan data; and serving the second series of scan data to the external entity.

Abstract: One variation of a method for updating a localization map for a fleet of autonomous vehicles includes: selecting a set of roads including a first subset of road segments associated with existing incomplete scan data and a second subset of road segments associated with a monitoring request from an external entity; during a passenger period at the autonomous vehicle, autonomously transporting passengers according to a series of ride requests; during a mapping period succeeding the passenger period at the autonomous vehicle, autonomously navigating along the set of road segments, recording a first series of scan data representing surfaces proximal the first subset of road segments, and recording a second series of scan data representing surfaces proximal the second subset of road segments; updating the localization map based on the first series of scan data; and serving the second series of scan data to the external entity.