Abstract
Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center.
Highlights
In recent years, there has been great interest in the modeling and analysis of interior building structures using laser range finders (LRFs)
We propose to utilize the information contained in the occupancy grid map as a geometric prior and only detect loop closures in locations that are suitable for scan matching
By detecting loop closures based on a grid map representation of the environment, we leverage the information contained in the occupancy grid map and ensure that constraints are detected only in locations that are well conditioned for scan matching
Summary
There has been great interest in the modeling and analysis of interior building structures using laser range finders (LRFs). The authors note that when GPS is unavailable or unreliable, the system must fall back to a combination of manual intervention and point cloud matching techniques to limit accumulated error Due to this constraint, this approach may not be suitable for automatic mobile mapping of large scale, GPS-denied environments. Because such methods do not incorporate prior geometric information, they are unable to prune false detections using line-of-sight or other geometric constraints To address these issues, we propose to utilize the information contained in the occupancy grid map as a geometric prior and only detect loop closures in locations that are suitable for scan matching. Since the particle filter computes a rough estimation of the position for each submap, we are able to derive an initial condition for the loop closure constraints and compute the metric transformation between locations by applying a genetic scan matching algorithm.
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