Abstract: On-board, computer-based systems and methods compute continuously updated, real-time state estimates for an aerial vehicle by appropriately combining, by a suitable Kalman filter, local, relative, continuous state estimates with global, absolute, noncontinuous state estimates. The local, relative, continuous state estimates can be provided by visual odometry (VO) and/or an inertial measurement unit (IMU). The global, absolute, noncontinuous state estimates can be provided by terrain-referenced navigation, such as map-matching, and GNSS. The systems and methods can provide the real-time, continuous estimates even when reliable GNSS coordinate data is not available.

Abstract: A method for mapping an environment comprises moving a sensor along a path from a start location (P0) through the environment, the sensor generating a sequence of images, each image associated with a respective estimated sensor location and comprising a point cloud having a plurality of vertices, each vertex comprising an (x,y,z)-tuple and image information for the tuple. The sequence of estimated sensor locations is sampled to provide a pose graph (P) comprising a linked sequence of nodes, each corresponding to a respective estimated sensor location. For each node of the pose graph (P), a respective cloud slice (C) comprising at least of portion of the point cloud for the sampled sensor location is acquired. A drift between an actual sensor location (Pi+1) and an estimated sensor location (Pi) on the path is determined.

Abstract: A method for mapping an environment comprises moving a sensor along a path from a start location (P0) through the environment, the sensor generating a sequence of images, each image associated with a respective estimated sensor location and comprising a point cloud having a plurality of vertices, each vertex comprising an (x,y,z)-tuple and image information for the tuple. The sequence of estimated sensor locations is sampled to provide a pose graph (P) comprising a linked sequence of nodes, each corresponding to a respective estimated sensor location. For each node of the pose graph (P), a respective cloud slice (C) comprising at least of portion of the point cloud for the sampled sensor location is acquired. A drift between an actual sensor location (Pi+1) and an estimated sensor location (Pi) on the path is determined.

Abstract: In a method for determining a translation and a rotation of a platform, at least a first frame and a previous frame are generated. Points are matched between images generated by two stereoscopic sensors. Points are matched to corresponding stereo feature matches between two frames, thereby generating a set of putative matches. Putative matches that are nearer to the platform than a threshold are categorized as near features. Putative matches that are farther to the platform than the threshold are categorized as distance features. The rotation of the platform is determined by measuring a positional change in two of the distant features. The translation of the platform is determined by compensating one of the near features for the rotation and then measuring a change in one of the near features measured between the first frame and the second frame.