Abstract: A computer-implemented method for localizing a vehicle can include accessing, by a computing system comprising one or more computing devices, a machine-learned retrieval model that has been trained using a ground truth dataset comprising a plurality of pre-localized sensor observations. Each of the plurality of pre-localized sensor observations has a predetermined pose value associated with a previously obtained sensor reading representation. The method also includes obtaining, by the computing system, a current sensor reading representation obtained by one or more sensors located at the vehicle. The method also includes inputting, by the computing system, the current sensor reading representation into the machine-learned retrieval model.

Abstract: Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.

Abstract: Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.

Abstract: A computer-implemented method for localizing a vehicle can include accessing, by a computing system comprising one or more computing devices, a machine-learned retrieval model that has been trained using a ground truth dataset comprising a plurality of pre-localized sensor observations. Each of the plurality of pre-localized sensor observations has a predetermined pose value associated with a previously obtained sensor reading representation. The method also includes obtaining, by the computing system, a current sensor reading representation obtained by one or more sensors located at the vehicle. The method also includes inputting, by the computing system, the current sensor reading representation into the machine-learned retrieval model.

Abstract: Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.

Abstract: The present disclosure discloses a method of preparing a carbon-coated ceria hollow sphere, which includes the following steps of: S110, dispersing silica in a solvent to obtain a silica dispersion; S120, performing a hydrothermal reaction between the silica dispersion and a cerium salt to obtain a ceria-coated silica microsphere; S140, coating the ceria-coated silica microsphere with a carbon source to obtain a primary product, wherein the carbon source is dopamine; S160, sintering the primary product under a protective gas atmosphere to obtain a carbon-coated ceria microsphere; and S170, etching the carbon-coated ceria microsphere by using an etchant to obtain a carbon-coated ceria hollow sphere.

Abstract: The present disclosure discloses a method of preparing a carbon-coated ceria hollow sphere, which includes the following steps of: S110, dispersing silica in a solvent to obtain a silica dispersion; S120, performing a hydrothermal reaction between the silica dispersion and a cerium salt to obtain a ceria-coated silica microsphere; S140, coating the ceria-coated silica microsphere with a carbon source to obtain a primary product, wherein the carbon source is dopamine; S160, sintering the primary product under a protective gas atmosphere to obtain a carbon-coated ceria microsphere; and S170, etching the carbon-coated ceria microsphere by using an etchant to obtain a carbon-coated ceria hollow sphere.