A method of traversing a vehicle transportation network , the method comprising: determining vehicle operational information of a vehicle , including sensing a position of the vehicle in global positioning system coordinates, wherein the vehicle is an autonomous vehicle or a semi-autonomous vehicle; determining a metric location estimate of the vehicle using the vehicle operational information, including determining the metric location estimate using the sensed position as input to a non-linear loss function with a Kalman filter to mitigate effects of un-modeled sensor error, wherein the non-linear loss function is a function that compares the sensed position to a projected position for the same temporal point, and selectively penalizes the sensed position for use within the Kalman filter such that a penalty applied to the sensed position increases with distance from the projected position, and the metric location estimate is an output of the Kalman filter; determining operational environment information of a portion of the vehicle transportation network, the operational environment information including sensor data of a portion of the vehicle transportation network that is observable to the vehicle; determining a topological location estimate of the vehicle in a coordinate system of a topological map, the topological map including the vehicle transportation network, using the metric location estimate and the operational environment information, wherein the topological location estimate comprises a particular lane within a particular multi-lane road of the vehicle transportation network; and traversing , by the vehicle, the vehicle transportation network using the topological location estimate as an input to a control system for the vehicle. determining vehicle operational information of a vehicle , including sensing a position of the vehicle in global positioning system coordinates, wherein the vehicle is an autonomous vehicle or a semi-autonomous vehicle; determining a metric location estimate of the vehicle using the vehicle operational information, including determining the metric location estimate using the sensed position as input to a non-linear loss function with a Kalman filter to mitigate effects of un-modeled sensor error, wherein the non-linear loss function is a function that compares the sensed position to a projected position for the same temporal point, and selectively penalizes the sensed position for use within the Kalman filter such that a penalty applied to the sensed position increases with distance from the projected position, and the metric location estimate is an output of the Kalman filter; determining operational environment information of a portion of the vehicle transportation network, the operational environment information including sensor data of a portion of the vehicle transportation network that is observable to the vehicle; determining a topological location estimate of the vehicle in a coordinate system of a topological map, the topological map including the vehicle transportation network, using the metric location estimate and the operational environment information, wherein the topological location estimate comprises a particular lane within a particular multi-lane road of the vehicle transportation network; and traversing , by the vehicle, the vehicle transportation network using the topological location estimate as an input to a control system for the vehicle. <EOS>