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Acquiring accurate three-dimensional depth information conventionallyrequires expensive multibeam LiDAR devices. Recently, researchers havedeveloped a less expensive option by predicting depth information fromtwo-dimensional color imagery. However, there still exists a substantial gap inaccuracy between depth information estimated from two-dimensional images andreal LiDAR point-cloud. In this paper, we introduce a fusion-based depthprediction method, called FusionMapping. This is the first method that fusescolored imagery and two-dimensional laser scan to estimate depth in-formation.More specifically, we propose an autoencoder-based depth prediction network anda novel point-cloud refinement network for depth estimation. We analyze theperformance of our FusionMapping approach on the KITTI LiDAR odometry datasetand an indoor mobile robot system. The results show that our introducedapproach estimates depth with better accuracy when compared to existingmethods.

FusionMapping: Learning Depth Prediction with Monocular Images and 2D Laser Scans