The synergy between Pd atoms, CNT and MnO ₂ gives rises to significantly enhanced activity, selectivity and stability of Pd/MnO ₂ –CNT for oxygen electrocatalysis. The assembled rechargeable metal air battery delivered high performance with enhanced cycling stability.

The ability to perceive humans is an essential requirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability of commercial color-depth cameras allow for the creation of a system that makes use of the depth dimension, thus enabling a robot to observe its environment and perceive in the 3-D space. Here we present a system for 3-D multiple human perception in real time from a moving robot equipped with a color-depth camera and a consumer-grade computer. Our approach reduces computation time to achieve real-time performance through a unique combination of new ideas and established techniques. We remove the ground and ceiling planes from the 3-D point cloud input to separate candidate point clusters. We introduce the novel information concept, depth of interest, which we use to identify candidates for detection, and that avoids the computationally expensive scanning-window methods of other approaches. We utilize a cascade of detectors to distinguish humans from objects, in which we make intelligent reuse of intermediary features in successive detectors to improve computation. Because of the high computational cost of some methods, we represent our candidate tracking algorithm with a decision directed acyclic graph, which allows us to use the most computationally intense techniques only where necessary. We detail the successful implementation of our novel approach on a mobile robot and examine its performance in scenarios with real-world challenges, including occlusion, robot motion, nonupright humans, humans leaving and reentering the field of view (i.e., the reidentification challenge), human-object and human-human interaction. We conclude with the observation that the incorporation of the depth information, together with the use of modern techniques in new ways, we are able to create an accurate system for real-time 3-D perception of humans by a mobile robot.

Human activity recognition has a variety of important real-world applications, such as video analysis, surveillance, and human-robot interaction. As a promising video representation method, local spatio-temporal (LST) features have received increasing attention from computer vision, machine learning, and robotics communities. However, approaches based on traditional LST features only use color information, which face several challenges, such as illumination changes and dynamic backgrounds. The recent availability of commercial color-depth cameras makes it much cheaper, faster, and easier to acquire depth information, which provides a potential to implement more discriminative and robust LST features. In this paper, we introduce the new 4-D color-depth (CoDe4D) LST feature that incorporates both intensity and depth information acquired from RGB-D cameras. Our feature detector constructs a saliency map through applying independent filters in xyzt dimension to represent texture, shape and pose variations, and selects its local maxima as interest points. Our multichannel orientation histogram descriptor applies a 4-D support region, which is adaptive to linear perspective view changes, on each interest point. Then, image gradients of color-depth patches within the support region are computed and quantized using a spherical coordinate-based method to form a final feature vector. We build a complete activity recognition system by combining our features with bag-of-features representations and support vector machines. To evaluate the performance of our CoDe4D LST features and the complete system, we conduct experiments using four benchmark color-depth human activity data sets, including UTK Action3-D, Berkeley MHAD, ACT4², and MSR daily activity 3-D data sets. Experimental results demonstrate the promising representative power of our CoDe4D features, which obtain the state-of-the-art performance on activity recognition from RGB-D visual data.

Place recognition, or loop closure detection, is an essential component to address the problem of visual simultaneous localization and mapping (SLAM). Long-term navigation of robots in outdoor environments introduces new challenges to enable life-long SLAM, including the strong appearance change resulting from vegetation, weather, and illumination variations across various times of the day, different days, months, or even seasons. In this paper, we propose a new shared representative appearance learning (SRAL) approach to address long-term visual place recognition. Different from previous methods using a single feature modality or a concatenation of multiple features, our SRAL method autonomously learns representative features that are shared in all scene scenarios, and then fuses the features together to represent the long-term appearance of environments observed by a robot during life-long navigation. By formulating SRAL as a regularized optimization problem, we use structured sparsity-inducing norms to model interrelationships of feature modalities. In addition, an optimization algorithm is developed to efficiently solve the formulated optimization problem, which holds a theoretical convergence guarantee. Extensive empirical study was performed to evaluate the SRAL method using large-scale benchmark datasets, including St Lucia, CMU-VL, and Nordland datasets. Experimental results have shown that our SRAL method obtains superior performance for life-long place recognition using individual images, outperforms previous single image-based methods, and is capable of estimating the importance of feature modalities.

We present a novel feature description algorithm to describe 3D local spatio-temporal features for human action recognition. Our descriptor avoids the singularity and limited discrimination power issues of traditional 3D descriptors by quantizing and describing visual features in the simplex topological vector space. Specifically, given a feature's support region containing a set of 3D visual cues, we decompose the cues' orientation into three angles, transform the decomposed angles into the simplex space, and describe them in such a space. Then, quadrant decomposition is performed to improve discrimination, and a final feature vector is composed from the resulting histograms. We develop intuitive visualization tools for analyzing feature characteristics in the simplex topological vector space. Experimental results demonstrate that our novel simplex-based orientation decomposition (SOD) descriptor substantially outperforms traditional 3D descriptors for the KTH, UCF Sport, and Hollywood-2 benchmark action datasets. In addition, the results show that our SOD descriptor is a superior individual descriptor for action recognition.