According to the present disclosure, a map matching apparatus includes a map information acquisition unit configured to acquire map information expressed by a plurality of links, a movement information acquisition unit configured to acquire movement information of a moving body on a predetermined route, and a matching unit configured to specify a link string, which is a string of the links corresponding to the route, based on the map information and the movement information, in which the matching unit is configured to generate a physical network, which is a road network, based on the connection relationship and the map information, generate a hierarchical logical network in which the physical network is duplicated in a plurality of layers and configured in a hierarchical manner, and specify the link string indicating a minimized cost in the hierarchical logical network.

A system for associating perceived and mapped lane edges can include a processor and a memory. The memory includes instructions such that the processor is configured to receive a sensor data representing a perceived object; receive map data representing a map object; determine a cost matrix a cost matrix indicative of an association cost for associating the map object to the perceived object; compare the association cost with an association cost threshold; and associate the perceived object with the map object based on the association cost.

A system for generating linear feature data is provided. The system may determine, from sensor data, detection data associated with at least one link. The at least one link comprises a plurality of sub links. The system may further determine, using map data, one or more linear feature clusters for each of the plurality of sub links, based on the detection data. Furthermore, the system may determine a plurality of linear feature groups for the at least one link, based on at least one set of feature matched distances and the determined linear feature clusters, where a given linear feature group respectively comprises at least one first linear feature cluster associated with a first sub link and at least one second linear feature cluster associated with a second sub link. Furthermore, the system may generate the linear feature data, based on the plurality of linear feature groups.

A system for generating linear feature data is provided. The system may determine, from sensor data, detection data associated with at least one link. The at least one link comprises a plurality of sub links. The system may further determine, using map data, one or more linear feature clusters for each of the plurality of sub links, based on the detection data. Furthermore, the system may determine a plurality of linear feature groups for the at least one link, based on at least one set of feature matched distances and the determined linear feature clusters, where a given linear feature group respectively comprises at least one first linear feature cluster associated with a first sub link and at least one second linear feature cluster associated with a second sub link. Furthermore, the system may generate the linear feature data, based on the plurality of linear feature groups.

An autonomous robotic vehicle is capable of detecting, identifying, and locating the source of gas leaks such as methane. Because of the number of operating components within the vehicle, it may also be considered a robotic system. The robotic vehicle can be remotely operated or can move autonomously within a jobsite. The vehicle selectively deploys a source detection device that precisely locates the source of a leak. The vehicle relays data to stakeholders and remains powered that enables operation of the vehicle over an extended period. Monitoring and control of the vehicle is enabled through a software interface viewable to a user on a mobile communications device or personal computer.

A method includes determining a first route from a first location to a second location using a first map that includes first map elements, wherein the first route includes a series of the first map elements from the first map. The method also includes matching the series of the first map elements from the first route to second map elements from a second map to define a subset of the second map elements, and determining a second route from the first location to the second location using the subset of the second map elements. The second route is constrained to the subset of the second map elements and the second route includes a series of the second map elements from the subset of the second map elements. The method also includes outputting information describing the second route for at least one of storage or display.

A method includes determining a first route from a first location to a second location using a first map that includes first map elements, wherein the first route includes a series of the first map elements from the first map. The method also includes matching the series of the first map elements from the first route to second map elements from a second map to define a subset of the second map elements, and determining a second route from the first location to the second location using the subset of the second map elements. The second route is constrained to the subset of the second map elements and the second route includes a series of the second map elements from the subset of the second map elements. The method also includes outputting information describing the second route for at least one of storage or display.

Described are systems and methods to utilize images to determine the position and/or orientation of a vehicle (e.g., an autonomous ground vehicle) operating in an unstructured environment (e.g., environments such as sidewalks which are typically absent lane markings, road markings, etc.). The described systems and methods can determine the vehicle's position and orientation based on an alignment of annotated images captured during operation of the vehicle with a known annotated reference map. The translation and rotation applied to obtain alignment of the annotated images with the known annotated reference map can provide the position and the orientation of the vehicle.

A localization and mapping method is for localizing and mapping a moving apparatus in a moving process. The localization and mapping method includes an image capturing step, a feature point extracting step, a flag object identifying step, and a localizing and mapping step. The image capturing step includes capturing an image frame at a time point of a plurality of time points in the moving process by a camera unit. The flag object identifying step includes identifying whether the image frame includes a flag object among a plurality of the feature points in accordance with a flag database. The flag database includes a plurality of dynamic objects, and the flag object is corresponding to one of the dynamic objects. The localizing and mapping step includes performing localization and mapping in accordance with the image frames captured and the flag object thereof in the moving process.

An approach is provided for pattern-based map matching of a probe trace to a digital map. The approach involves querying the digital map for a set of road links within a threshold distance of a probe point. The approach also involves determining a match starting point for each road link of a set of road links. The approach further involves selecting a sampled probe point from the probe trace. The approach also involves generating one or more patterns for said each road link based on the match starting point, the sampled probe point, the topology polyline, or a combination thereof. The approach further involves selecting a matched road link from among the set of road links based on the one or more patterns. The probe point is then snapped to the matched road link.