Cooperative Vision-based Localization Networks with Communication Constraints

TL;DR

This paper proposes a cooperative vision-based localization scheme for vehicles that accounts for communication constraints, optimizing bit allocation and demonstrating improved localization accuracy through a novel VGD algorithm.

Contribution

It introduces a new cooperative localization framework with communication constraints, deriving the Fisher information matrix and a bit allocation method, along with a VGD algorithm for enhanced performance.

Findings

Proposed algorithm outperforms conventional methods in localization accuracy.

Numerical results show higher computational efficiency of the proposed method.

The scheme effectively integrates vision and distance measurements under communication constraints.

Abstract

Accurate location information is indispensable for the emerging applications of \ac{iov}, such as automatic driving and formation control. In the real scenario, vision-based localization has demonstrated superior performance to other localization methods for its stability and flexibility. In this paper, a scheme of cooperative vision-based localization with communication constraints is proposed. Vehicles collect images of the environment and distance measurements between each other. Then vehicles transmit the coordinates of feature points and distances with constrained bits to the edge to estimate their positions. The \ac{fim} for absolute localization is first obtained, based on which we derive the relative \ac{speb} through subspace projection. Furthermore, we formulate the corresponding bit allocation problem for relative localization. Finally, a \ac{vgd} algorithm is developed by considering the influence of photographing, distance measurements and quantization noises. Compared with conventional bit allocation methods, numerical results demonstrate the localization performance gain of our proposed algorithm with higher computational efficiency.