Lidars can be extremely useful tools for measuring outdoor geometry. However while lidar measurements are championed for their high accuracy their point clouds are individually rather sparse and lack colour information. In this work the sparse nature of lidar point clouds is addressed by merging multiple lidar scans into a single large point cloud. This is done by restricting the lidar motion to a single axis of translation and then using interpolation and iterative refinement to acquire a denser model by combining co-registered sets of point clouds. This newly constructed model is then used to guide a basic stereo SLAM (simultaneous localization and mapping) algorithm in order to produce a final dense coloured point cloud that preserves the accuracy of the original lidar measurements. Our experiments were performed at various locations using a 16 channel “Puck” Velodyne lidar and a stereo acquisition system consisting of a DJI Phantom quadcopter and a synchronized pair of GoPro HERO 3+ black edition cameras. Results of these experiments demonstrate that the produced reconstructions are both ascetically sound and quantitatively consistent with a set of individual measurements taken around the scene.
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Gee et al. (2016) studied this question.