Multi-robot Cooperative Localization Research Articles

Consistent batch fusion for decentralized multi-robot cooperative localization

Consistent batch fusion for decentralized multi-robot cooperative localization

Multi-Robot Cooperative Localization: Time-Varying Unobservable Subspace Analysis and Consistent Algorithm Design

Multi-Robot Cooperative Localization: Time-Varying Unobservable Subspace Analysis and Consistent Algorithm Design

Resilient Cooperative Localization Based on Factor Graphs for Multirobot Systems

With the advancement of intelligent perception in multirobot systems, cooperative localization in dynamic environments has become a critical component. However, existing multirobot cooperative localization systems still fall short in meeting high-precision localization requirements in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a factor-graph-based resilient cooperative localization (FG-RCL) algorithm for multirobot systems. This algorithm integrates measurements from visual sensors and Ultra-WideBand (UWB) to achieve accurate cooperative state estimation—overcoming the visibility issues of visual sensors within limited fields of view. We utilize the Joint Probabilistic Data Association (JPDA) algorithm to calculate the corresponding probabilities of multiple visual detection measurements between robots and assign them to their respective edges in the factor graph, thereby addressing the data association challenges in visual detection measurements. Finally, simulation results demonstrate that the proposed algorithm significantly reduces the influence of visual detection measurement interference on the performance of cooperative localization. Experimental results indicate that the proposed algorithm outperforms UWB-based and vision-based methods in terms of localization accuracy. The system is implemented using a factor-graph-based optimization approach, and it exhibits scalability and enables plug-and-play for sensors. Furthermore, it demonstrates resilience in abnormal situations.

Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization.

Ultra-wideband (UWB) localization methods have emerged as a cost-effective and accurate solution for GNSS-denied environments. There is a significant amount of previous research in terms of resilience of UWB ranging, with non-line-of-sight and multipath detection methods. However, little attention has been paid to resilience against disturbances in relative localization systems involving multiple nodes. This paper presents an approach to detecting range anomalies in UWB ranging measurements from the perspective of multi-robot cooperative localization. We introduce an approach to exploiting redundancy for relative localization in multi-robot systems, where the position of each node is calculated using different subsets of available data. This enables us to effectively identify nodes that present ranging anomalies and eliminate their effect within the cooperative localization scheme. We analyze anomalies created by timing errors in the ranging process, e.g., owing to malfunctioning hardware. However, our method is generic and can be extended to other types of ranging anomalies. Our approach results in a more resilient cooperative localization framework with a negligible impact in terms of the computational workload.

Nonlinear observability analysis of multi-robot cooperative localization

In this paper, the observability property of relative position-based cooperative localization is investigated by using spectral graph theory. First, a directed graph, called Extended Relative Measurement Graph (ERMG), is constructed in which all influencing factors of observability are integrated. Then, the rank equivalence is established between the nonlinear observability matrix and the configuration matrix associated with a reduced ERMG. Then, a necessary and sufficient observability condition is proved. Moreover, an analytic relation is derived by which the configuration matrix can be directly attained according to fundamental cycles of a reduced ERMG. Then, it is shown that the observability is uniquely determined by the number and placement of fundamental cycles in a reduced ERMG. The minimum number of fundamental cycles required to guarantee an n -robot system observable is given. Additionally, a sufficient condition is provided by which reduced ERMGs can be designed to enable an n -robot system observable. Finally, a graph-based observability checking algorithm is developed which greatly improves the computation efficiency. Some illustrative examples are presented to validate the theoretical results.

Resilient and Consistent Multirobot Cooperative Localization With Covariance Intersection

Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple inter-robot communication with observation, making these algorithms susceptible to failures in both communication and observation steps. To enhance the resilience of multirobot cooperative localization algorithms in a distributed system, we use covariance intersection to formalize a localization algorithm with an explicit communication update and ensure estimation consistency at the same time. We investigate the covariance boundedness criterion of our algorithm with respect to communication and observation graphs, demonstrating provable localization performance under even sparse communications topologies. We substantiate the resilience of our algorithm as well as the boundedness analysis through experiments on simulated and benchmark physical data against varying communications connectivity and failure metrics. Especially when inter-robot communication is entirely blocked or partially unavailable, we demonstrate that our method is less affected and maintains desired performance compared to existing cooperative localization algorithms.

Fault Tolerant Multi-Robot Cooperative Localization Based on Covariance Union

This paper studies the multi-robot cooperative localization (CL) problem, a challenging scenario in which robots may receive spurious sensor data, potentially causing inconsistent state estimates. To address this problem, this paper presents a fully decentralized CL algorithm based on covariance union (CU), referred to as DCL-CU. The proposed approach is fault-tolerant and supports generic measurement models. Extensive Monte Carlo simulations and a group of real-world experiments were conducted to verify the performance of the proposed DCL-CU approach. The results show that the DCL-CU approach can efficiently deal with spurious sensor data.

Multirobot Cooperative Localization Based on Visible Light Positioning and Odometer

With the development of robotics, multirobot collaboration system (MRCS) has become a popular focus of research in recent years. As the key technology of MRCS, multirobot cooperative localization (MRCL) is becoming more and more popular due to its higher accuracy and robustness by exchanging and sharing information. In this article, we propose an MRCL system based on visible light positioning (VLP) technology and odometer (VO-MRCL). Using rolling shutter camera and odometer, the system relaxes the quantity required for observable-light emitting diode (LED) lamps of each robot to one and improves the robustness of the VLP-based robot positioning methods under LED shortage. The accuracy of the proposed VO-MRCL system is verified by real-world experiments, where a two-robot system is proposed as an example specifically. The results of experiment show that our proposed system is feasible and can achieve the positioning accuracy with an average error of 4.31 cm.

Event based distributed kalman filter for limited resource multirobot cooperative localization

AbstractThis paper presents a multirobot cooperative event based localization scheme with improved bandwidth usage in a heterogeneous group of mobile robots. The proposed method relies on an agent based framework that defines the communications between robots and on an event based Extended Kalman Filter that performs the cooperative sensor fusion from local, global and relative sources. The event is generated when the pose error covariance exceeds a predefined limit. By this, the robots update the pose using the relative information available only when necessary, using less bandwidth and computational resources when compared to the time based methods, allowing bandwidth allocation for other tasks while extending battery life. The method is tested using a simulation platform developed in the programming language JAVA with a group of differential mobile robots represented by an agent in a JADE framework. The pose estimation performance, error covariance and number of messages exchanged in the communication are measured and used to compare the traditional time based approach with the proposed event based algorithm. Also, the compromise between the accuracy of the localization method and the bandwidth usage is analyzed for different event limits. A final experimental test with two SUMMIT XL robots is shown to validate the simulation results.

Optimal Scheduling for Resource-Constrained Multirobot Cooperative Localization

Typically, localization algorithms focus on maximizing estimation accuracy, often while neglecting some of the real-world costs of achieving such performance. In this letter, we formulate optimal scheduling problems of multirobot localization to investigate the tradeoff between estimate accuracy and its corresponding cost. We derive the continuous-time approximation to explicitly represent the rates of distinct operations in a multirobot localization scheme, which we then use to define the optimization problems for scheduling those operations. For a given accuracy requirement, we can solve the cost minimization problem to find the lowest cost schedule satisfying the prescribed error expectation; similarly, for a given cost budget, we can solve the trace minimization problem to find a schedule for the best achievable error performance. While the interplay between the localization error and the operation parameters is described by a Riccati equation, the Frechet derivative is applied to shed the light on their implicit relationship. In a localization example grounded in a realistic setting, we show that by solving these optimal scheduling problems, power consumption can be reduced by $64\%$ through cost minimization, or the bounding covariance trace can be reduced by $15\%$ through trace minimization.

Cooperative localization and evaluation of small-scaled spherical underwater robots

With the goal of supporting localization requirements of our spherical underwater robots, such as multi robot cooperation and intelligent biological surveillance, a cooperative localization system of multi robot was designed and implemented in this study. Given the restrictions presented by the underwater environment and the small-sized spherical robot, an time of flight camera and microelectro mechanical systems (MEMS) sensor information fusion algorithm using coordinate normalization transfer models were adopted to construct the proposed system. To handle the problem of short location distance, limited range under fixed view of camera in the underwater environment, a MEMS inertial sensor was used to obtain the attitude information of robot and expanding the range of underwater visual positioning, the transmission of positioning information could implement through the normalization of absolute coordinate, then the positioning distance increased and realized the localization of multi robot system. Given the environmental disturbances in practical underwater scenarios, the Kalman filter model was used to minimizing the systematic positioning error. Based on the theoretical analysis and calculation, we describe experiments in underwater to evaluate the performance of cooperative localization. The experimental results confirmed the validity of the multi robot cooperative localization system proposed in this paper, and the distance of cooperative localization system proposed in this paper is larger than the visual positioning system we have developed previously.

An Online Scalable Approach to Unified Multirobot Cooperative Localization and Object Tracking

In this paper, we present a unified approach for multi-robot cooperative simultaneous localization and object tracking based on particle filters. Our approach is scalable with respect to the number of robots in the team. We introduce a method that reduces, from an exponential to a linear growth, the space and computation time requirements with respect to the number of robots in order to maintain a given level of accuracy in the full-state estimation. Our method requires no increase in the number of particles with respect to the number of robots. However, in our method, each particle represents a full-state hypothesis, leading to the linear dependency on the number of robots of both space and time complexity. The derivation of the algorithm implementing our approach from a standard particle filter algorithm and its complexity analysis are presented. Through an extensive set of simulation experiments on a large number of randomized datasets, we demonstrate the correctness and efficacy of our approach. Through real robot experiments on a standardized open dataset of a team of four soccer-playing robots tracking a ball, we evaluate our method's estimation accuracy with respect to the ground truth values. Through comparisons with other methods based on 1) nonlinear least squares minimization and 2) joint extended Kalman filter, we further highlight our method's advantages. Finally, we also present a robustness test for our approach by evaluating it under scenarios of communication and vision failure in teammate robots.

Moving-horizon nonlinear least squares-based multirobot cooperative perception

In this article we present an online estimator for multirobot cooperative localization and target tracking based on nonlinear least squares minimization. Our method not only makes the rigorous optimization-based approach applicable online but also allows the estimator to be stable and convergent. We do so by employing a moving horizon technique to nonlinear least squares minimization and a novel design of the arrival cost function that ensures stability and convergence of the estimator. Through an extensive set of real robot experiments, we demonstrate the robustness of our method as well as the optimality of the arrival cost function. The experiments include comparisons of our method with (i) an extended Kalman filter-based online-estimator and (ii) an offline-estimator based on full-trajectory nonlinear least squares.

Decentralized Cooperative Localization Approach for Autonomous Multirobot Systems

This study proposes the use of a split covariance intersection algorithm (Split-CI) for decentralized multirobot cooperative localization. In the proposed method, each robot maintains a local cubature Kalman filter to estimate its own pose in a predefined coordinate frame. When a robot receives pose information from neighbouring robots, it employs a Split-CI based approach to fuse this received measurement with its local belief. The computational and communicative complexities of the proposed algorithm increase linearly with the number of robots in the multirobot systems (MRS). The proposed method does not require fully connected synchronous communication channels between robots; in fact, it is applicable for MRS with asynchronous and partially connected communication networks. The pose estimation error of the proposed method is bounded. As the proposed method is capable of handling independent and interdependent information of the estimations separately, it does not generate overconfidence state estimations. The performance of the proposed method is compared with several multirobot localization approaches. The simulation and experiment results demonstrate that the proposed algorithm outperforms the single-robot localization algorithms and achieves approximately the same estimation accuracy as the centralized cooperative localization approach, but with reduced computational and communicative cost.

A robust extended [formula omitted] filtering approach to multi-robot cooperative localization in dynamic indoor environments

Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended H∞ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.

A “thermodynamic” approach to multi-robot cooperative localization

We propose a new approach to the simultaneous cooperative localization of a very large group of simple robots capable of performing dead-reckoning and sensing the relative position of nearby robots. In the last decade, the use of distributed optimal Kalman filters (KF) to address this problem has been studied extensively. In this paper, we propose to use a very simple encounter based averaging process (denoted by EA). The idea behind EA is the following: every time two robots meet, they simply average their location estimates.We assume that two robots meet whenever they are close enough to allow relative location estimation and communication. At each meeting event, the robots average their location estimations thus reducing the localization error. Naturally, the frequency of the meetings affects the localization quality. The meetings are determined by the robots’ movement pattern. In this work we consider movement patterns which are “well mixing”, i.e. every robot meets other robots and eventually all of the robots frequently. For such a movement pattern, the time course of the expected localization error is derived. We prove that EA is asymptotically optimal and requires significantly less computation and communication resources than KF.

The UTIAS multi-robot cooperative localization and mapping dataset

This paper presents a two-dimensional multi-robot cooperative localization and mapping dataset collection for research and educational purposes. The dataset consists of nine sub-datasets, which can be used for studying problems such as robot-only cooperative localization, cooperative localization with a known map, and cooperative simultaneous localization and mapping (SLAM) . The data collection process is discussed in detail, including the equipment we used, how measurements were made and logged, and how we obtained groundtruth data for all robots and landmarks. The format of each file in each sub-dataset is also provided. The dataset is available for download at http://asrl.utias.utoronto.ca/datasets/mrclam/.

Multi-robot cooperative localization based on autonomous motion state estimation and laser data interaction

Multi-robot cooperative localization is an important research topic in mobile robotics. This paper mainly studies the problem of cooperative localization based on environment information interaction in unknown complex environment. Considering the effect of robot motion state on the cooperative localization, we present an algorithm for adaptive selecting periods of the cooperative localization based on autonomous motion state estimation. In order to avoid equipping an extra localization marker on the robot which is needed for direct observation between robots, and to make full use of the interactive information between robots in the same scene, a method for obtaining multi-robot observations based on MbICP algorithm is proposed, which makes multirobot cooperative localization using EKF feasible and reliable in cluttered environment. Experiment results with the Pioneer 3-DX robots and data analysis show the method’s validity and practicability.

Observability-based consistent EKF estimators for multi-robot cooperative localization

In this paper, we investigate the consistency of extended Kalman filter (EKF)-based cooperative localization (CL) from the perspective of observability. We analytically show that the error-state system model employed in the standard EKF-based CL always has an observable subspace of higher dimension than that of the actual nonlinear CL system. This results in unjustified reduction of the EKF covariance estimates in directions of the state space where no information is available, and thus leads to inconsistency. To address this problem, we adopt an observability-based methodology for designing consistent estimators in which the linearization points are selected to ensure a linearized system model with observable subspace of correct dimension. In particular, we propose two novel observability-constrained (OC)-EKF estimators that are instances of this paradigm. In the first, termed OC-EKF 1.0, the filter Jacobians are calculated using the prior state estimates as the linearization points. In the second, termed OC-EKF 2.0, the linearization points are selected so as to minimize their expected errors (i.e., the difference between the linearization point and the true state) under the observability constraints. The proposed OC-EKFs have been tested in simulation and experimentally, and have been shown to significantly outperform the standard EKF in terms of both accuracy and consistency.

Performance analysis of multirobot Cooperative localization

This paper studies the accuracy of position estimation for groups of mobile robots performing cooperative localization. We consider the case of teams comprised of possibly heterogeneous robots and provide analytical expressions for the upper bound on their expected positioning uncertainty. This bound is determined as a function of the sensors' noise covariance and the eigenvalues of the relative position measurement graph (RPMG), i.e., the weighted directed graph which represents the network of robot-to-robot exteroceptive measurements. The RPMG is employed as a key element in this analysis, and its properties are related to the localization performance of the team. It is shown that, for a robot group of a certain size, the maximum expected rate of uncertainty increase is independent of the accuracy and number of relative position measurements and depends only on the accuracy of the proprioceptive and orientation sensors on the robots. Additionally, the effects of changes in the topology of the RPMG are studied, and it is shown that, at steady-state, these reconfigurations do not inflict any loss in localization precision. Experimental data, as well as simulation results that validate the theoretical analysis, are presented