A navigation system for positioning a vehicle, comprising: a communication interface configured to receive a plurality of point cloud frames of a scene and initial pose data of the vehicle captured by a plurality of sensors (140, 150, 160) equipped on the vehicle as the vehicle moves along a trajectory; a storage configured to store the plurality of point cloud frames and the initial pose data; and a processor configured to: estimate pose information of the vehicle associated with each of the plurality of point cloud frames based on the initial pose data and the plurality of point cloud frames using Unscented Kalman Filter, UKF, wherein the processor is configured to pick a minimal set of sample points around a posterior mean using a deterministic sampling technique based on the plurality of point cloud frames and the initial pose data, and propagate the minimal set of sample points through a nonlinear function; adjust the estimated pose information of the vehicle based on a model, wherein the model includes a spatial relationship and a temporal relationship among the plurality of point cloud frames, and wherein to adjust the estimated pose information of the vehicle, the plurality of point cloud frames is divided into a plurality of batches based on the spatial relationship and the temporal relationship and a set of thresholds and the processor is configured to adjust the estimated pose information of the vehicle according to the plurality of batches; and position the vehicle based on the adjusted pose information. a communication interface configured to receive a plurality of point cloud frames of a scene and initial pose data of the vehicle captured by a plurality of sensors (140, 150, 160) equipped on the vehicle as the vehicle moves along a trajectory; a storage configured to store the plurality of point cloud frames and the initial pose data; and a processor configured to: estimate pose information of the vehicle associated with each of the plurality of point cloud frames based on the initial pose data and the plurality of point cloud frames using Unscented Kalman Filter, UKF, wherein the processor is configured to pick a minimal set of sample points around a posterior mean using a deterministic sampling technique based on the plurality of point cloud frames and the initial pose data, and propagate the minimal set of sample points through a nonlinear function; adjust the estimated pose information of the vehicle based on a model, wherein the model includes a spatial relationship and a temporal relationship among the plurality of point cloud frames, and wherein to adjust the estimated pose information of the vehicle, the plurality of point cloud frames is divided into a plurality of batches based on the spatial relationship and the temporal relationship and a set of thresholds and the processor is configured to adjust the estimated pose information of the vehicle according to the plurality of batches; and position the vehicle based on the adjusted pose information. estimate pose information of the vehicle associated with each of the plurality of point cloud frames based on the initial pose data and the plurality of point cloud frames using Unscented Kalman Filter, UKF, wherein the processor is configured to pick a minimal set of sample points around a posterior mean using a deterministic sampling technique based on the plurality of point cloud frames and the initial pose data, and propagate the minimal set of sample points through a nonlinear function; adjust the estimated pose information of the vehicle based on a model, wherein the model includes a spatial relationship and a temporal relationship among the plurality of point cloud frames, and wherein to adjust the estimated pose information of the vehicle, the plurality of point cloud frames is divided into a plurality of batches based on the spatial relationship and the temporal relationship and a set of thresholds and the processor is configured to adjust the estimated pose information of the vehicle according to the plurality of batches; and position the vehicle based on the adjusted pose information.