Metric Scale Calculation for Visual Mapping Algorithms

Abstract

Visual SLAM algorithms allow localizing the camera by mapping its environment by a point cloud based on visual cues. To obtain the camera locations in a metric coordinate system, the metric scale of the point cloud has to be known. This contribution describes a method to calculate the metric scale for a point cloud of an indoor environment, like a parking garage, by fusing multiple individual scale values. The individual scale values are calculated from structures and objects with a-priori known metric extension, which can be identified in the unscaled point cloud. Extensions of building structures, like the driving lane or the room height, are derived from density peaks in the point distribution. The extension of objects, like traffic signs with a known metric size, are derived using projections of their detections in images onto the point cloud. The method is tested with synthetic image sequences of a drive with a front-looking mono camera through a virtual 3D model of a parking garage. It has been shown, that each individual scale value improves either the robustness of the fused scale value or reduces its error. The error of the fused scale is comparable to other recent works.

Monocular Visual SLAM Based on a Cooperative UAV–Target System

“…The scale factor s is given by [ 51 ]: where is the real distance, and is the estimated distance. For the monocular SLAM problem, there exist different kind of distances and lots of data for real (and estimated) distances: distances between camera and landmarks, distances between landmarks, distances defined by the positions of the camera in time periods (camera trajectory), among other distances.…”