Abstract: A method for extracting road data comprises obtaining ground data from first environment detection data of a detected region. A first ground model may be obtained using the ground data. A missing region of the first ground model may be filled to obtain a second ground model. Road attributes of the detected region may be obtained according to the second ground model. Road damage recognition may be performed using second environment detection data of the detected region and the second ground model. The method may include storing the road attributes and the result of the road damage recognition as pieces of road data of the detected region.

Abstract: A method for extracting road data comprises obtaining ground data from first environment detection data of a detected region, obtaining a first ground model using the ground data, filling a missing region of the first ground model to obtain a second ground model, obtaining road attributes of the detected region according to the second ground model, performing road damage recognition using second environment detection data of the detected region and the second ground model, and storing the road attributes and the result of the road damage recognition as pieces of road data of the detected region.

Abstract: A vehicle ranging system includes a ranging scanner, a ranging data processor, and an object classifier. The ranging scanner may be configured to generate a series of ranging data. The ranging data processor may be configured to apply a scale invariant ranging template to the series of ranging data and determine a difference between the ranging template and the series of ranging data. The object classifier may be configured to identify an object corresponding to the series of ranging data based on the difference between the ranging template and the series of ranging data.

Abstract: A ranging template system includes a ranging data interface, a sampler, a processor, and a database. The ranging data interface may be configured to receive a series of ranging data. The sampler may be configured to identify a subset of the series of ranging data. The processor may be configured to associate an object with the subset of ranging data and generate a scale invariant ranging template based on the subset of ranging. The database may be configured to store the scale invariant ranging template.

Abstract: A vehicle ranging system includes a ranging scanner, a ranging data processor, and an object classifier. The ranging scanner may be configured to generate a series of ranging data. The ranging data processor may be configured to apply a scale invariant ranging template to the series of ranging data and determine a difference between the ranging template and the series of ranging data. The object classifier may be configured to identify an object corresponding to the series of ranging data based on the difference between the ranging template and the series of ranging data.

Abstract: An approach is provided for regularizing building footprints. The approach, for example, involves designating a vertex of an initial building footprint as an initial vertex of a regularized footprint. The approach also involves generating an edge from the initial vertex to a next vertex of the initial footprint to add to the regularized footprint when that the edge can connect along a taxicab distance. The taxicab distance, for instance, is based on a rectilinear geometry between two points. The approach further involves adding a new vertex to the regularized footprint when the edge cannot connect along the taxicab distance. The edge then connects the initial vertex to the new vertex, and another edge connects the new vertex to the next vertex along the taxicab distance. The approach further involves generating a subsequent new edge or vertex of the regularized footprint for each subsequent next vertex of the initial footprint.

Abstract: Embodiments described herein relate generally to determining correspondence between a template and an object in an image. A method may include: receiving an image of an environment including an image of an object within the image of the environment; resizing the first template to obtain a scaled first template having a size corresponding to a size of the image of the object; calculating a number of correspondences between the scaled first template and the image of the object; receiving a candidate homography; testing the candidate homography; and replacing the image of the object with a second template of a different object according to the candidate homography in response to the candidate homography being established as corresponding to the image of the object.

Abstract: An approach is provided for regularizing building footprints. The approach, for example, involves designating a vertex of an initial building footprint as an initial vertex of a regularized footprint. The approach also involves generating an edge from the initial vertex to a next vertex of the initial footprint to add to the regularized footprint when that the edge can connect along a taxicab distance. The taxicab distance, for instance, is based on a rectilinear geometry between two points. The approach further involves adding a new vertex to the regularized footprint when the edge cannot connect along the taxicab distance. The edge then connects the initial vertex to the new vertex, and another edge connects the new vertex to the next vertex along the taxicab distance. The approach further involves generating a subsequent new edge or vertex of the regularized footprint for each subsequent next vertex of the initial footprint.

Abstract: An approach is provided for generating a cleaned object model to represent an object in a mapping database. The approach includes receiving point cloud data depicting the object. The approach also includes processing the point cloud data to determine one or more surface points of the point cloud data. The one or more surface points represent one or more surfaces of the object. The approach further includes cutting a model of the object into one or more fragments using the one or more surface points. The one or more fragments include one or more object fragments and one or more non-object fragments. The approach further includes designating the one or more object fragments as the cleaned object model to represent the object in the mapping database.

Abstract: Embodiments described herein relate generally to determining correspondence between a template and an object in an image. A method may include: receiving an image of an environment including an image of an object within the image of the environment; resizing the first template to obtain a scaled first template having a size corresponding to a size of the image of the object; calculating a number of correspondences between the scaled first template and the image of the object; receiving a candidate homography; testing the candidate homography; and replacing the image of the object with a second template of a different object according to the candidate homography in response to the candidate homography being established as corresponding to the image of the object.

Abstract: An approach is provided for generating a cleaned object model to represent an object in a mapping database. The approach includes receiving point cloud data depicting the object. The approach also includes processing the point cloud data to determine one or more surface points of the point cloud data. The one or more surface points represent one or more surfaces of the object. The approach further includes cutting a model of the object into one or more fragments using the one or more surface points. The one or more fragments include one or more object fragments and one or more non-object fragments. The approach further includes designating the one or more object fragments as the cleaned object model to represent the object in the mapping database.

Abstract: In one embodiment, image detection is improved or accelerated using an approximate range query to classify images. A controller is trained on a set of training feature vectors. The training feature vectors represent an image. The feature vectors are normalized to a uniform length. The controller defines a matching space that includes the set of training feature vectors. The controller is configured to identify whether an input vector for a tested image falls within the matching space based on a range query. When the input vector falls within the matching space, the tested image substantially matches the portion of the image used to train the controller.

Abstract: Systems, devices, features, and methods for determining geographic features corresponding to a travel path to develop a map database, such as a navigation database, are disclosed. For example, one method comprises emitting light from a light source, such as a LIDAR device, while on the travel path. Returning light is received based on the emitted light. The returning light is used to generate data points representing an area about the travel path. The data points are filtered as a function of a return intensity value to identify a feature associated with the travel path, in which the feature is treated with a retroreflective substance.

Abstract: In one embodiment, image detection is improved or accelerated using an approximate range query to classify images. A controller is trained on a set of training feature vectors. The training feature vectors represent an image. The feature vectors are normalized to a uniform length. The controller defines a matching space that includes the set of training feature vectors. The controller is configured to identify whether an input vector for a tested image falls within the matching space based on a range query. When the input vector falls within the matching space, the tested image substantially matches the portion of the image used to train the controller.

Abstract: In one embodiment, image detection is improved or accelerated using an approximate range query to classify images. A controller is trained on a set of training feature vectors. The training feature vectors represent an image. The feature vectors are normalized to a uniform length. The controller defines a matching space that includes the set of training feature vectors. The controller is configured to identify whether an input vector for a tested image falls within the matching space based on a range query. When the input vector falls within the matching space, the tested image substantially matches the portion of the image used to train the controller.

Abstract: An absorbing composition is described herein that includes at least one inorganic-based compound, at least one absorbing compound, and at least one material modification agent. In addition, methods of making an absorbing composition are also described that includes: a) combining at least one inorganic-based compound, at least one absorbing compound, at least one material modification agent, an acid/water mixture, and one or more solvents to form a reaction mixture; and b) allowing the reaction mixture to form the absorbing composition at room temperature. Another method of making an absorbing composition includes: a) combining at least one inorganic-based compound, at least one absorbing compound, at least one material modification agent, an acid/water mixture, and one or more solvents to form a reaction mixture; and b) heating the reaction mixture to form the absorbing composition.

Abstract: The present invention addresses provides improved methods of preparing a low-k dielectric material on a substrate. The methods involve multi-step ultraviolet curing processes in which UV intensity, wafer substrate temperature and other conditions may be independently modulated at each step. In certain embodiments, a film containing a structure former and a porogen is exposed to UV radiation in a first step to facilitate removal of the porogen and create a porous dielectric film. In a second step, the film is exposed to UV radiation to increase crosslinking within the porous film. In certain embodiments, the curing takes place in a multi-station UV chamber wherein UV intensity and substrate temperature may be independently controlled at each station.

Abstract: In one embodiment, image detection is improved or accelerated using an approximate range query to classify images. A controller is trained on a set of training feature vectors. The training feature vectors represent an image. The feature vectors are normalized to a uniform length. The controller defines a matching space that includes the set of training feature vectors. The controller is configured to identify whether an input vector for a tested image falls within the matching space based on a range query. When the input vector falls within the matching space, the tested image substantially matches the portion of the image used to train the controller.

Abstract: Systems, devices, features, and methods for determining geographic features corresponding to a travel path to develop a map database, such as a navigation database, are disclosed. For example, one method comprises emitting light from a light source, such as a LIDAR device, while on the travel path. Returning light is received based on the emitted light. The returning light is used to generate data points representing an area about the travel path. The data points are filtered as a function of a return intensity value to identify a feature associated with the travel path, in which the feature is treated with a retroreflective substance.

Abstract: Systems, methods, and apparatus for depositing a tantalum layer on a wafer substrate are disclosed. In one aspect, a tantalum layer may be deposited on a surface of a wafer substrate using an ion-induced atomic layer deposition process with a tantalum precursor. A copper layer may be deposited on the tantalum layer.