Adaptive Baseline Monocular Dense Mapping with Inter-Frame Depth Propagation

Abstract

State-of-the-art monocular dense mapping methods usually divide the image sequence into several separate multi-view stereo problems thus have limited utilization of the information in multi-baseline observations and sequential depth estimations. In this paper, two core contributions are proposed to improve the mapping performance by exploiting the information. The first is an adaptive baseline matching cost computation that uses the sequential input images to provide each pixel with wide-baseline observations. The second is a frame-to-frame propagated depth filter which integrates the sequential depth estimation of the same physical point in a robust probabilistic manner. Two contributions are integrated into a monocular dense mapping system that generates the depth maps in real-time for both pinhole and fisheye cameras. Our system is fully parallelized and can run at more than 25 fps on a Nvidia Jetson TX2. We compare our work with state-of-the-art methods on the public dataset. Onboard UAV mapping and handhold experiments are also used to demonstrate the performance of our method. For the benefit of the community, we make the implementation open source.