A hybrid estimation framework for cooperative localization under communication constraints

Abstract

Abstract-In this paper, we consider the problem of multicentralized Cooperative Localization (CL) under severe communication constraints, i.e., when each robot can communicate only a single bit per real-valued (analog) measurement. Existing approaches, such as those based on the Sign-of-Innovation Kalman filter (SOI-KF) and its variants, require each robot to process quantized versions of both its local (i.e., recorded by its own sensors) and remote (i.e., collected by other robots) measurements. This results in suboptimal performance since each robot has to discard information that is available in its own analog measurements. To address this limitation, we introduce a novel hybrid estimation scheme that enables each robot to process both quantized (from remote sensors) and analog (from its own sensors) measurements. Specifically, we first present the hybrid (H)-SOI-KF, a direct extension of the SOI-KF, for processing both types of measurements. Secondly, we introduce the modified (M)H-SOI-KF, that uses an asymmetric encoding/decoding scheme to incorporate additional information during quantization (based on the hybrid estimates locally available to each robot), resulting in substantial accuracy improvement. Lastly, we present extensive simulations which demonstrate that both hybrid estimators not only outperform the SOI-KF, but also achieve accuracy comparable to that of the standard (analog) centralized Kalman filter.

A communication-bandwidth-aware hybrid estimation framework for multi-robot cooperative localization

“…Next, note that the structures of the IQKF and H-IQKF, are strikingly similar to that of the single-bit SOI-KF [14] and H-SOIKF [18], respectively, where the analog measurement is quantized to a single bit. Specifically, as expected, when p = 1, the IQKF and H-IQKF are identical to SOI-KF and H-SOIKF, respectively.…”

“…In order to address this problem, we introduced a novel hybrid estimation framework in [18] that enables each robot to process all available measurement information, i.e., local analog and remote quantized measurements. To achieve this, each robot i maintains two estimators (see (1) a quantized Q-estimator that processes quantized measurements from all robots including itself, i.e., (2) a hybrid H-estimator that processes its own analog measurements and quantized measurements from the other robots in the team, i.e.,x Hi…”

“…If the difference between the predicted measurements of the H-BQKF and BQKF is large, the quantized measurement will convey very little information to the H-BQKF. 5) By choosing f = 1 and substituting the corresponding optimal thresholds τ j k (1) = −∞, τ j k (2) = 0, and τ j k (3) = ∞ in Proposition 1, we obtain the special case of the single bit H-estimator in [18].…”

“…In [18], we introduced a hybrid estimation framework that addresses this problem by maintaining two local estimators for each robot (see Fig. 1): (i) a quantized Q-estimator processing quantized measurements from all robots, including itself, and (ii) a hybrid H-estimator processing its own analog measurements along with the quantized measurements from other robots in the team.…”

“…1): (i) a quantized Q-estimator processing quantized measurements from all robots, including itself, and (ii) a hybrid H-estimator processing its own analog measurements along with the quantized measurements from other robots in the team. The H-estimator in [18] is designed for the special/restrictive scenario when robots communicate only a single bit per analog measurement.…”