Error Scaling Laws for Linear Optimal Estimation From Relative Measurements

Error Scaling Laws for Optimal Estimation from Relative Measurements

We study the problem of estimating vector-valued variables from noisy “relative” measurements. This problem arises in several sensor network applications. The measurement model can be expressed in terms of a graph, whose nodes correspond to the variables and edges to noisy measurements of the difference between two variables. We take an arbitrary variable as the reference and consider the optim...

Error Scaling in Position Estimation from Noisy Relative Pose Measurements

We examine how fast the estimation error grows with time when a mobile robot/vehicle estimates its location from relative pose measurements without global position or orientation sensors. We show that both bias and variance of the position estimation error grows at most linearly with time (or distance traversed) asymptotically. The bias growth rate is crucially dependent on the trajectory of th...

Error growth in position estimation from noisy relative pose measurements

We examine how the estimation error grows with time when a mobile robot estimates its location from relative pose measurements without global position or orientation sensors. We show that in both 2-D or 3-D space, both the bias and variance of the position estimation error grows at most linearly with time (or distance traversed) asymptotically. The bias is crucially dependent on the trajectory ...

Rapid estimation of sunflower leaf area from linear measurements

Distributed centroid estimation from noisy relative measurements

We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisy relative measurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents positions are estimated with respect to the anchor. We...