Abstract: Relocating and/or re-sizing map elements using an updated pose graph without introducing abnormalities to the map data may comprise determining a transformation between a source node of a first pose graph and a target node of a second pose graph and determining a modification to a map element based at least in part on the transformation. The techniques may include determining a stress on the map based at least in part on one or more modifications to map elements and determining if the stress meets or exceeds a threshold. In instances where the stress meets or exceeds a threshold, a modification may be altered, reversed, and/or indicated in a notification transmitted to a user interface.

Abstract: Techniques for integrating sensor data into a scene or map based on statistical data of captured environmental data are discussed herein. The data may be stored as a multi-resolution voxel space and the techniques may comprise first applying a pre-alignment or localization technique prior to fully integrating the sensor data.

Abstract: Techniques are described for determining whether a point cloud registration (e.g., alignment) between two sets of data points is valid. Such techniques can include determining voxelized representations of the sets of data points, and comparing characteristics of spatially aligned voxels within the voxelized representations. Characteristics of voxels to be compared can include classification labels of data points associated with voxels, including whether or not voxels correspond to free space. Point cloud registrations determined to be invalid can be given a weighting to be used in a subsequent high definition (HD) map building process. Generated maps can then be deployed for use in autonomous vehicles.

Abstract: Techniques associated with generating and maintaining sparse geographic and map data. In some cases, the system may maintain a factor graph comprising a plurality of nodes. In some cases, the nodes may comprise pose data and sensor data associated with an autonomous vehicle at the geographic position represented by the node. The nodes may be linked based on shared trajectories and shared sensor data.

Abstract: Techniques are described for determining whether a point cloud registration (e.g., alignment) between two sets of data points is valid. Such techniques can include determining voxelized representations of the sets of data points, and comparing characteristics of spatially aligned voxels within the voxelized representations. Characteristics of voxels to be compared can include classification labels of data points associated with voxels, including whether or not voxels correspond to free space. Point cloud registrations determined to be invalid can be given a weighting to be used in a subsequent high definition (HD) map building process. Generated maps can then be deployed for use in autonomous vehicles.

Abstract: Techniques associated with generating and maintaining sparse geographic and map data. In some cases, the system may maintain a factor graph comprising a plurality of nodes. In some cases, the nodes may comprise pose data and sensor data associated with an autonomous vehicle at the geographic position represented by the node. The nodes may be linked based on shared trajectories and shared sensor data.

Abstract: Techniques for determining a location of a vehicle in an environment using sensors and determining calibration information associated with the sensors are discussed herein. A vehicle can use map data to traverse an environment. The map data can include semantic map objects such as traffic lights, lane markings, etc. The vehicle can use a sensor, such as an image sensor, to capture sensor data. Semantic map objects can be projected into the sensor data and matched with object(s) in the sensor data. Such semantic objects can be represented as a center point and covariance data. A distance or likelihood associated with the projected semantic map object and the sensed object can be optimized to determine a location of the vehicle. Sensed objects can be determined to be the same based on matching with the semantic map object. Epipolar geometry can be used to determine if sensors are capturing consistent data.

Abstract: Techniques are discussed for using multi-resolution maps, for example, for localizing a vehicle. Map data of an environment can be represented by discrete map tiles. In some cases, a set of map tiles can be precomputed as contributing to localizing the vehicle in the environment, and accordingly, the set of map tiles can be loaded into memory when the vehicle is at a particular location in the environment. Further, a level of detail represented by the map tiles can be based at least in part on a distance between a location associated with the vehicle and a location associated with a respective region in the environment. The level of detail can also be based on a speed of the vehicle in the environment. The vehicle can determine its location in the environment based on the map tiles and/or the vehicle can generate a trajectory based on the map tiles.

Abstract: Techniques associated with generating and maintaining sparse geographic and map data. In some cases, the system may maintain a factor graph comprising a plurality of nodes. In some cases, the nodes may comprise pose data and sensor data associated with an autonomous vehicle at the geographic position represented by the node. The nodes may be linked based on shared trajectories and shared sensor data.

Abstract: Techniques for determining a location of a vehicle in an environment using sensors and determining calibration information associated with the sensors are discussed herein. A vehicle can use map data to traverse an environment. The map data can include semantic map objects such as traffic lights, lane markings, etc. The vehicle can use a sensor, such as an image sensor, to capture sensor data. Semantic map objects can be projected into the sensor data and matched with object(s) in the sensor data. Such semantic objects can be represented as a center point and covariance data. A distance or likelihood associated with the projected semantic map object and the sensed object can be optimized to determine a location of the vehicle. Sensed objects can be determined to be the same based on matching with the semantic map object. Epipolar geometry can be used to determine if sensors are capturing consistent data.

Abstract: Techniques for determining a location of a vehicle in an environment using sensors and determining calibration information associated with the sensors are discussed herein. A vehicle can use map data to traverse an environment. The map data can include semantic map objects such as traffic lights, lane markings, etc. The vehicle can use a sensor, such as an image sensor, to capture sensor data. Semantic map objects can be projected into the sensor data and matched with object(s) in the sensor data. Such semantic objects can be represented as a center point and covariance data. A distance or likelihood associated with the projected semantic map object and the sensed object can be optimized to determine a location of the vehicle. Sensed objects can be determined to be the same based on matching with the semantic map object. Epipolar geometry can be used to determine if sensors are capturing consistent data.

Abstract: A vehicle may include a localization and/or mapping component to understand what surrounds the autonomous vehicle and where it is in relation to the surroundings. The localization and/or mapping component may receive sensor data from sensor(s) of the vehicle and generate a map and/or position and/or orientation from the sensor data according to parameters that configure the way the localization and/or mapping component generates the map and/or position/orientation. A computing device may dynamically adjust these parameters, thereby changing the way the map and/or position/orientation are generated. This adjustment may be based on a condition detected from the sensor data and may increase a clarity (e.g., degree of distinctness/clarity) of the generated map and/or position/orientation.

Abstract: Techniques are discussed for using multi-resolution maps, for example, for localizing a vehicle. Map data of an environment can be represented by discrete map tiles. In some cases, a set of map tiles can be precomputed as contributing to localizing the vehicle in the environment, and accordingly, the set of map tiles can be loaded into memory when the vehicle is at a particular location in the environment. Further, a level of detail represented by the map tiles can be based at least in part on a distance between a location associated with the vehicle and a location associated with a respective region in the environment. The level of detail can also be based on a speed of the vehicle in the environment. The vehicle can determine its location in the environment based on the map tiles and/or the vehicle can generate a trajectory based on the map tiles.

Abstract: Techniques are discussed for using multi-resolution maps, for example, for localizing a vehicle. Map data of an environment can be represented by discrete map tiles. In some cases, a set of map tiles can be precomputed as contributing to localizing the vehicle in the environment, and accordingly, the set of map tiles can be loaded into memory when the vehicle is at a particular location in the environment. Further, a level of detail represented by the map tiles can be based at least in part on a distance between a location associated with the vehicle and a location associated with a respective region in the environment. The level of detail can also be based on a speed of the vehicle in the environment. The vehicle can determine its location in the environment based on the map tiles and/or the vehicle can generate a trajectory based on the map tiles.

Abstract: Techniques are discussed for using multi-resolution maps, for example, for localizing a vehicle. Map data of an environment can be represented by discrete map tiles. In some cases, a set of map tiles can be precomputed as contributing to localizing the vehicle in the environment, and accordingly, the set of map tiles can be loaded into memory when the vehicle is at a particular location in the environment. Further, a level of detail represented by the map tiles can be based at least in part on a distance between a location associated with the vehicle and a location associated with a respective region in the environment. The level of detail can also be based on a speed of the vehicle in the environment. The vehicle can determine its location in the environment based on the map tiles and/or the vehicle can generate a trajectory based on the map tiles.