Keith Yu Kit Leung

Decentralized Localization of Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Finite-range sensing and communication are factors in the connectivity of a dynamic mobile-robot network. State estimation becomes a difficult problem when communication connections allowing information exchange between all robots are not guaranteed. This paper presents a decentralized state-estimation algorithm guaranteed to work in dynamic robot networks without connectivity requirements. We prove that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible while ensuring that the same can be performed by all other robots in the network. We prove certain properties of our technique and then it is validated through simulations. We present a comprehensive set of results, indicating the performance benefit in different network connectivity settings, as well as the scalability of our approach.

Localization in urban environments by matching ground level video images with an aerial image

This paper presents the design of a monocular vision based particle filter localization system for urban settings that uses aerial orthoimagery as the reference map. One of the design objectives is to provide a low cost method for outdoor localization using a single camera. This relaxes the need for global positioning system (GPS) which may experience degraded reliability in urban settings. The second objective is to study the achievable localization performance with the aforementioned resources. Image processing techniques are employed to create a feature map from an aerial image, and also to extract features from camera images to provide observations that are used by a particle filter for localization.

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/.

Decentralized Cooperative SLAM for Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Communication between robots is key to performance in cooperative multi-robot systems. In practice, communication connections for information exchange between all robots are not always guaranteed, which adds difficulty in performing state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem, in which each robot is required to estimate the map and all robot states under a sparsely-communicating and dynamic network. We show how the exact, centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner even when the network is never fully connected. Furthermore, a robot only needs to consider its own knowledge of the network topology in order to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 min of hardware experiments using a team of real robots. The resulting estimates are compared against accurate groundtruth data for all robot poses and landmark positions. In addition, we examined the effects of communication range limit on our algorithm’s performance.

An improved weighting strategy for Rao-Blackwellized Probability Hypothesis Density simultaneous localization and mapping

The use of random finite sets (RFSs) in simultaneous localization and mapping (SLAM) for mobile robots is a new concept that provides several advantages over traditional vector-based approaches. These include: 1) the incorporation of detection statistics, as well as the usual spatial uncertainty, in an estimation algorithm, 2) the ability to estimate the number of landmarks in a map, and 3) the circumvention of the need for data association heuristics. Solutions to SLAM can be obtained through the Rao-Blackwellized Probability Hypothesis Density (RB-PHD) filter, which is an approximation of the Bayes filter for RFSs using both particles to represent the robot trajectories, and Gaussian mixtures to represent their associated maps. This paper proposes an improved multi-feature particle weighting strategy for the RB-PHD filter and shows through simulations that it outperforms existing weighting strategies. The proposed strategy makes the RB-PHD filter a generalization of multi-hypothesis (MH) FastSLAM, a vector-based SLAM solution that uses the RB-particle filter.

Decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks

This paper presents a simultaneous localization and mapping (SLAM) algorithm that allows a recursive state estimation process to be both distributed and decentralized in a sparse robot network that is never guaranteed to be fully connected (communication-wise). In such a sparse network, a robot may not always have the latest odometry and measurements from other robots. Our approach allows robots to obtain a temporary (localization and map) estimate at the current timestep using information available locally, but we also ensure that the centralized-equivalent estimate can always be recovered by all robots at a later time; we do not require a robot to keep track of what other robots know when it applies the Markov property to discard past information. Our method is validated through a hardware SLAM experiment where we distribute data association hypotheses amongst a team of robots. Estimate errors are shown to validate the performance of our approach. We also discuss the trade-offs and show comparisons between our distributed approach versus a non-distributed one.

Decentralized localization for dynamic and sparse robot networks

Finite-range sensing and communication are factors in the connectivity of a dynamic mobile robot network. State estimation becomes a difficult problem when communication connections for information exchange between all robots are not guaranteed. This paper presents a decentralized state estimation algorithm guaranteed to work in dynamic networks without connectivity requirements. We show that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible, while ensuring the same can be performed by all other robots in the network. Our technique is validated through simulations.

Generalizing random-vector SLAM with random finite sets

The simultaneous localization and mapping (SLAM) problem in mobile robotics has traditionally been formulated using random vectors. Alternatively, random finite sets(RFSs) can be used in the formulation, which incorporates non-heursitic-based data association and detection statistics within an estimator that provides both spatial and cardinality estimates of landmarks. This paper mathematically shows that the two formulations are actually closely related, and that RFS SLAM can be viewed as a generalization of vector-based SLAM. Under a set of ideal detection conditions, the two methods are equivalent. This is validated by using simulations and real experimental data, by comparing principled realizations of the two formulations.

Metrics for Evaluating Feature-Based Mapping Performance

In robotic mapping and simultaneous localization and mapping, the ability to assess the quality of estimated maps is crucial. While concepts exist for quantifying the error in the estimated trajectory of a robot, or a subset of the estimated feature locations, the difference between all current estimated and ground-truth features is rarely considered jointly. In contrast to many current methods, this paper analyzes metrics, which automatically evaluate maps based on their joint detection and description uncertainty. In the tracking literature, the optimal subpattern assignment (OSPA) metric provided a solution to the problem of assessing target tracking algorithms and has recently been applied to the assessment of robotic maps. Despite its advantages over other metrics, the OSPA metric can saturate to a limiting value irrespective of the cardinality errors and it penalizes missed detections and false alarms in an unequal manner. This paper therefore introduces the cardinalized optimal linear assignment (COLA) metric, as a complement to the OSPA metric, for feature map evaluation. Their combination is shown to provide a robust solution for the evaluation of map estimation errors in an intuitive manner.

Multifeature-based importance weighting for the PHD SLAM filter

The probability-hypothesis-density simultaneous localization and mapping filter is a random-finite-set estimation method that incorporates the probability-hypothesis-density filter within a Rao–Blackwellized particle filter, and was developed for navigation and mapping problems. However, the filter tends to diverge due to the existing importance-weighting methods used in the Rao–Blackwellized particle filter. This article introduces a new importance-weighting method that drastically improves the robustness of the probability-hypothesis-density simultaneous localization and mapping filter. Performance evaluations are conducted using both simulations and real experimental data sets.