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.

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.

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.

Communication among 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 to state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem under a sparsely-communicating and dynamic network. We mathematically prove how the centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner. Furthermore, a robot only needs to consider its own knowledge of the network topology to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 minutes of experiments using a team of real robots, with accurate groundtruth data of all robots and landmark features.

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.

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.

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 majority of today's navigation techniques for intelligent transportation systems use global positioning systems (GPS) that can provide position information with bounded errors. However, due to the low accuracy that is experienced with standard GPS, it is difficult to determine a vehicle's position at lane level. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within communication range, the lane positions of vehicles can be found. The algorithm's effectiveness is shown in both simulations and experiments with real data.