Motion compensation for lidar tomography and remote sensing

Lidar tomography is a method that constructs high-resolution images of objects from multiple range projections along different projection axes. This approach is one way to overcome traditional limitations in remote sensing with focal imaging such as diffraction, optical aberrations, and air turbulence. We have shown previously through detailed modelling and simulation that lidar tomography can generate resolved imagery of objects from a moving platform if sufficient diversity of view angles and appropriate geolocation accuracy requirements can be met. Here we show that the geolocation accuracy requirements can be met through a data-driven approach that does not require accurate knowledge of the platform's position relative to the object being imaged. This alleviates a significant technical burden of motion tracking and opens the way for a more practical implementation of the lidar tomography technique for remote sensing and imaging.