Keyframe-based dense planar SLAM

Abstract

In this work, we develop a novel keyframe-based dense planar SLAM (KDP-SLAM) system, based on CPU only, to reconstruct large indoor environments in real-time using a hand-held RGB-D sensor. Our keyframe-based approach applies a fast dense method to estimate odometry, fuses depth measurements from small baseline images, extracts planes from the fused depth map, and optimizes the poses of the keyframes and landmark planes in a global factor graph using incremental smoothing and mapping (iSAM). Using the fast odometry estimation, correct plane correspondences may be found projectively, and the pose of each frame can be estimated accurately even without sufficient planes to fully constrain the 6 degree-of-freedom transformation. The depth map generated from the local fusion process generates higher quality reconstructions and plane segmentations by eliminating noise. Moreover, explicitly modeling plane landmarks in the fully probabilistic global optimization significantly reduces the drift that plagues other dense SLAM algorithms. We test our system on standard RGB-D benchmarks as well as additional indoor environments, demonstrating its state-of-the-art performance as a real-time dense 3D SLAM algorithm, without the use of GPU.