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: 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: Localization error handling using output is described. A computing system associated with a vehicle can receive sensor data from a sensor associated with vehicle. The computing system can determine, based at least partly on the sensor data, a first instruction for controlling the vehicle during a first period of time and a difference in pose information associated with a pose of the vehicle. Based at least partly on determining the difference, the computing system can retrieve a second instruction for controlling the vehicle during a second period of time prior to the first period of time and, based at least partly on comparing the first instruction and the second instruction, the computing system can determine whether the vehicle is to follow the first instruction or perform an alternate operation.

Abstract: Techniques are discussed for determining a location of a vehicle in an environment using a feature corresponding to a portion of an image representing an object in the environment which is associated with a frequently occurring object classification. For example, an image may be received and semantically segmented to associate pixels of the image with a label representing an object of an object type (e.g., extracting only those portions of the image which represent lane boundary markings). Features may then be extracted, or otherwise determined, which are limited to those portions of the image. In some examples, map data indicating a previously mapped location of a corresponding portion of the object may be used to determine a difference. The difference (or sum of differences for multiple observations) are then used to localize the vehicle with respect to the map.

Abstract: Techniques are discussed for determining a location of a vehicle in an environment using a feature corresponding to a portion of an image representing an object in the environment which is associated with a frequently occurring object classification. For example, an image may be received and semantically segmented to associate pixels of the image with a label representing an object of an object type (e.g., extracting only those portions of the image which represent lane boundary markings). Features may then be extracted, or otherwise determined, which are limited to those portions of the image. In some examples, map data indicating a previously mapped location of a corresponding portion of the object may be used to determine a difference. The difference (or sum of differences for multiple observations) are then used to localize the vehicle with respect to the map.

Abstract: Techniques are discussed for determining a location of a vehicle in an environment using a feature corresponding to a portion of an image representing an object in the environment which is associated with a frequently occurring object classification. For example, an image may be received and semantically segmented to associate pixels of the image with a label representing an object of an object type (e.g., extracting only those portions of the image which represent lane boundary markings). Features may then be extracted, or otherwise determined, which are limited to those portions of the image. In some examples, map data indicating a previously mapped location of a corresponding portion of the object may be used to determine a difference. The difference (or sum of differences for multiple observations) are then used to localize the vehicle with respect to the map.

Abstract: Techniques are discussed for determining a location of a vehicle in an environment using a feature corresponding to a portion of an image representing an object in the environment which is associated with a frequently occurring object classification. For example, an image may be received and semantically segmented to associate pixels of the image with a label representing an object of an object type (e.g., extracting only those portions of the image which represent lane boundary markings). Features may then be extracted, or otherwise determined, which are limited to those portions of the image. In some examples, map data indicating a previously mapped location of a corresponding portion of the object may be used to determine a difference. The difference (or sum of differences for multiple observations) are then used to localize the vehicle with respect to the map.

Abstract: The invention provides a nutrient composition for augmenting immune strength or physiological detoxification. The nutrient composition consists of an optimal combination of an effective amount of at least one vitamin antioxidant, at least one mineral antioxidant and a highly saturable amount of at least three high potency antioxidants. The at least one vitamin antioxidant can be vitamin C, bioflavonoid complex, vitamin E, vitamin B6 or beta-carotene and the at least one mineral antioxidant can be zinc or selenium. The at least three high potency antioxidants can be alpha lipoic acid, acetyl L-carnitine, N-acetyl-cysteine, co-enzyme Q10 or glutathione. Also provided is a nutrient composition for augmenting immune strength or physiological detoxification that consists of an optimal combination of an effective amount of at least three vitamin antioxidants, at least two mineral antioxidants and a highly saturable amount of at least three high potency antioxidants.