Filtering out outlier data associations between local maps can improve the robustness and accuracy of multi-robot localization. When the overlap is low and the field of view difference is large, it is likely to produce outlier data associations between local maps, which will reduce the matching accuracy and even lead to the failure of collaborative localization. To solve this problem, this paper proposes a novel outdoor robust collaborative localization algorithm (HORCL) capable for high outlier ratio scenes. The Mixture Probability Model (MPM) and the Hierarchical EM (Expectation Maximization) algorithm in HORCL are applied to screen two levels of outliers (loop closure constraints and point pairs) and improve localization performance. Specifically, the inlier probabilities of data associations are calculated in MPM to identify outliers by considering geometric distances, semantic consistency, and spatial consistency. Then, outlier loop closures and outlier point pairs in inlier constraints are filtered by applying the Hierarchical EM algorithm, thereby relieving the adverse effect of outliers on localization accuracy. The proposed algorithm is validated on public datasets and compared with the latest methods, demonstrating the improvement in localization accuracy and robustness. The code is available at https://github.com/BIT-TYJ/HORCL.

Outlier feature matches and loop-closures that survived front-end data association can lead to catastrophic failures in the back-end optimization of large-scale point cloud based 3D reconstruction. To alleviate this problem, we propose a probabilistic approach for robust back-end optimization in the presence of outliers. More specifically, we model the problem as a Bayesian network and solve it using the Expectation-Maximization algorithm. Our approach leverages on a long-tail Cauchy distribution to suppress outlier feature matches in the odometry constraints, and a Cauchy-Uniform mixture model with a set of binary latent variables to simultaneously suppress outlier loop-closure constraints and outlier feature matches in the inlier loop-closure constraints. Furthermore, we show that by using a Gaussian-Uniform mixture model, our approach degenerates to the formulation of a state-of-the-art approach for robust indoor reconstruction. Experimental results demonstrate that our approach has comparable performance with the state-of-the-art on a benchmark indoor dataset, and outperforms it on a large-scale outdoor dataset. Our source code can be found on the project website.

Drone-mounted flying cameras will revolutionize photo-taking. The user, instead of holding a camera in hand and manually searching for a viewpoint, will interact directly with image contents in the viewfinder through simple gestures, and the flying camera will achieve the desired viewpoint through the autonomous flying capability of the drone. This work studies a common situation in photo-taking, i.e., the underlying viewpoint search problem for composing a photo with two objects of interest. We model it as a Perspective-2-Point (P2P) problem, which is under-constrained to determine the six degrees-of-freedom camera pose uniquely. By incorporating the user's composition requirements and minimizing the camera's flying distance, we form a constrained nonlinear optimization problem and solve it in closed form. Experiments on synthetic data sets and on a flying camera system indicate promising results.

Social media advertising is a multi-billion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for each user based on their personal interests. However, as user interests often evolve slowly, the user may end up receiving repetitive ads. In this paper, we propose a context-aware advertising framework that takes into account the relatively static personal interests as well as the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we first propose an online retrieval strategy that finds k most relevant ads matching the dynamic context when a read operation is triggered. To avoid frequent retrieval when the context varies little, we propose a safe region method to quickly determine whether the top-k ads of a user are changed. Finally, we propose a hybrid model to combine the merits of both methods by analyzing the dynamism of news feed to determine an appropriate retrieval strategy. Extensive experiments conducted on multiple real social networks and ad datasets verified the efficiency and robustness of our hybrid model.

This article explores the application of existing social media platforms for human–robot interaction. With the increasing popularity of social media platforms that connect humans, we propose to portray domestic robots as buddies on the contact list of family members and present a robot management system that employs complementary social media platforms for humans to interact with the vacuuming robot Roomba and a surveillance robot developed on top of iRobot Create. The social media platforms adopted include short message services (SMS), instant messenger (MSN), an online shared calendar (Google Calendar), and a social networking site (Facebook). Hence, we can provide a rich set of user-familiar, intuitive, and highly accessible interfaces, allowing users to flexibly choose their preferred tools in different situations. An in-lab experiment and a multiday field study are conducted to study the characteristics and strengths of each interface and to investigate users’ perception to the robots and behaviors in choosing the interfaces.