Mutual localization: Two camera relative 6-DOF pose estimation from reciprocal fiducial observation

Abstract

Abstract-Concurrently estimating the 6-DOF pose of multiple cameras or robots-cooperative localization-is a core problem in contemporary robotics. Current works focus on a set of mutually observable world landmarks and often require inbuilt egomotion estimates; situations in which both assumptions are violated often arise, for example, robots with erroneous low quality odometry and IMU exploring an unknown environment. In contrast to these existing works in cooperative localization, we propose a cooperative localization method, which we call mutual localization, that uses reciprocal observations of camera-fiducials to obviate the need for egomotion estimates and mutually observable world landmarks. We formulate and solve an algebraic formulation for the pose of the two camera mutual localization setup under these assumptions. Our experiments demonstrate the capabilities of our proposal egomotion-free cooperative localization method: for example, the method achieves 2cm range and 0.7 degree accuracy at 2m sensing for 6-DOF pose. To demonstrate the applicability of the proposed work, we deploy our method on Turtlebots and we compare our results with ARToolKit [1] and Bundler [2], over which our method achieves a tenfold improvement in translation estimation accuracy.

Fast Mutual Relative Localization of UAVs using Ultraviolet LED Markers

“…Numerous experimental solutions based on vision and mutual observation of UAV and UGVs equipped with known geometrical markers were tested [16], [17]. Our previous solution uses circular visual markers [2], [3] for mutual localization in small swarms of UAVs [5], [6] and in heterogeneous formations [4].…”

Underwater Surveying via Bearing only Cooperative Localization

“…Initially the problem was analytically solved in 2D [32], and then the analytical solution was extended in 3D [13]. At the same time, Dhima et al [33] produced a numerical solution, clearly a more computationally expensive and less accurate formulation. The analytical solution was further used to assist the flying formation of quadrotors [8].…”

Mutual Localization of UAVs based on Blinking Ultraviolet Markers and 3D Time-Position Hough Transform

“…In an indoor-only setup, colorbased markers can be used, see [9], [10], that are easy to segment under controlled lighting conditions, but not in the extremely unpredictable lighting conditions and the multicolored outdoor environment. For outdoors, black and white markers are preferred, leading to solutions which combine passive markers and object detection, see [11], [12], used for swarms [2], [13] and heterogeneous groups of robots [14], [15], [16]. The drawbacks of these approaches are the need for large markers, computational complexity and sensitivity to lighting conditions.…”