Real-time monocular SLAM: Why filter?

Abstract— While the most accurate solution to off-line structure from motion (SFM) problems is undoubtedly to extract as much correspondence information as possible and perform global optimisation, sequential methods suitable for live video streams must approximate this to fit within fixed computational bounds. Two quite different approaches to real-time SFM — also called monocular SLAM (Simultaneous Localisation and Mapping) — have proven successful, but they sparsify the problem in different ways. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods retain the optimisation approach of global bundle adjustment, but computationally must select only a small number of past frames to process. In this paper we perform the first rigorous analysis of the relative advantages of filtering and sparse optimisation for sequential monocular SLAM. A series of experiments in simulation as well using a ...

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Hauke Strasdat, J. M. M. Montiel, Andrew J. Davison
• Keywords: ICRA 2010, Monocular Slam, Robotics, Sequential Monocular Slam, first Rigorous Analysis