The visual fog paradigm envisions tens of thousands of heterogeneous, camera-enabled edge devices distributed across the Internet, providing live sensing for a myriad of different visual processing applications. The scale, computational demands, and bandwidth needed for visual computing pipelines necessitates offloading intelligently to distributed computing infrastructure, including the cloud, Internet gateway devices, and the edge devices themselves. This paper focuses on the visual fog scheduling problem of assigning the visual computing tasks to various devices to optimize network utilization. We first prove this problem is NP-complete, and then formulate a practical, efficient solution. We demonstrate sub-minute computation time to optimally schedule 20,000 tasks across over 7,000 devices, and just 7-minute execution time to place 60,000 tasks across 20,000 devices, showing our approach is ready to meet the scale challenges introduced by visual fog.

Wireless sensor networks (WSNs) have been widely deployed in various applications, e.g., agricultural monitoring and industrial monitoring, for their ease-of-deployment. The low-cost nature makes WSNs particularly vulnerable to changes of extrinsic factors, i.e., the environment, or changes of intrinsic factors, i.e., hardware or software failures. The problem can, often times, be uncovered via detecting unexpected behaviors (anomalies) of devices. However, anomaly detection in WSNs is subject to the following challenges: (1) the limited computation and connectivity, (2) the dynamicity of the environment and network topology, and (3) the need of taking real-time actions in response to anomalies. In this paper, we propose a novel framework using optimal weighted one-class random forests for unsupervised anomaly detection to address the aforementioned challenges in WSNs. The ample experiments showed that our framework not only is feasible but also outperforms the state-of-the-art unsupervised methods in terms of both detection accuracy and resource utilization.

Location tracking of passive RFID tags is useful for its ultra-low cost, but is very challenging for its passive nature. A passive RFID tag relies on no internal power source and draws power from the field created by the reader to power the microchip's circuits. This has made passive RFID tags highly sensitive to surrounding materials, as well as any disturbance. Therefore, conventional machine learning models may not perform well. In this paper, we propose an ensemble-based machine learning model, together with novel feature engineering techniques, for location tracking of passive RFID, which can work seamlessly with any supervised learning methods. The reader-based sub-models training method used in our model significantly reduces the training time by splitting our model into smaller sub-models and training them in parallel, which is desirable in real-world application.

Visual data are rich, which have opened vast analytics opportunities and been widely used in many applications. However, the demanding requirements of computational resources and bandwidth have prevented the data from being useful in an economically efficient manner. A visual fog paradigm is needed for efficient processing of continuous video streams by collaboratively using things in the Internet of Video Things (IoVT), comprising edge devices, intermediate gateways, and servers on premise or in the cloud, as the computing platform. The challenges lying ahead include (1) Reusability-a reusable framework across multiple vertical applications, (2) Efficiency-the intelligence for online distributing and redistributing work-load for optimal system performance, and (3) Configurability-the user interface for (layperson) users to easily analyze the visual data as well as the corresponding metadata. This paper spells out the need of a framework for visual fog computing and suggest promising research directions towards instantiations of a visual fog computing framework.

The paper addresses the revisiting (loop closing) problem of simultaneous localization and mapping (SLAM) by investigating spatio-temporal coherence in inertial and perceptual inputs to improve the robustness and convergence of SLAM. The basic idea is to find out coherent subsequences of confidence in trajectory to ensure against error-prone correspondences. It is achieved by leveraging fuzzy matching based on local trajectory structure and measurement similarity. Our approach does not rely on any global features or propagation modeling, which can be unreliable in the presence of gross errors and result in divergence. Apart from WiFi SLAM, our approach can also be capable of improving generic SLAM problems by leveraging spatio-temporal coherence. The experiments show that our approach can significantly reduce the ambiguity in WiFi fingerprinting, and subsequently lead to performance improvement in terms of mapping and localization.

Localization and mapping are fundamental tasks in mobile robotics. State-of-the-arts often rely on the static world assumption using the occupancy grids. However, the real environment is typically dynamic. We propose the feasibility grids to facilitate the representation of both the static scene and the moving objects. The dual sensor models are introduced to discriminate between stationary and moving objects in mobile robot localization. Instead of estimating the occupancy states, the feasibility grids maintain the stochastic estimates of the feasibility (crossability) states of the environment. Given that an observation can be decomposed into stationary objects and moving objects, incorporating the feasibility grids in localization yields performance improvements over the occupancy grids, particularly in highly dynamic environments. Our approach is extensively evaluated using real data acquired with a planar laser range finder. The experimental results show that the feasibility grid is capable of rapid convergence and robust performance in mobile robot localization by taking into account moving object information. A root mean squares accuracy of within 50 cm is achieved, without the aid of GPS, which is sufficient for autonomous navigation in crowded urban scenes. The empirical results suggest that the performance of localization can be improved when handling the changing environment explicitly. I.

The iterative closest points (ICP) algorithm is widely used for ego-motion estimation in robotics, but subject to bias in the presence of outliers. We propose a random sample consensus (RANSAC) based algorithm to simultaneously achieving robust and realtime ego-motion estimation, and multi-scale segmentation in environments with rapid changes. Instead of directly sampling on measurements, RANSAC matching investigates initial estimates at the object level of abstraction for systematic sampling and computational efficiency. A soft segmentation method using a multi-scale representation is exploited to eliminate segmentation errors. By explicitly taking into account the various noise sources degrading the effectiveness of geometric alignment: sensor noise, dynamic objects and data association uncertainty, the uncertainty of a relative pose estimate is calculated under a theoretical investigation of scoring in the RANSAC paradigm. The improved segmentation can also be used as the basis for higher level scene understanding. The effectiveness of our approach is demonstrated qualitatively and quantitatively through extensive experiments using real data.

Robust ego-motion estimation in urban environments is a key prerequisite for making a robot truly autonomous, but is not easily achievable as there are two motions involved: the motions of moving objects and the motion of the robot itself. We proposed a random sample consensus (RANSAC) based ego-motion estimator to deal with highly dynamic environments using one planar laser scanner. Instead of directly sampling on individual measurements, the RANSAC process is performed at a higher level abstraction for systematic sampling and computational efficiency. We proposed a multiple-model approach to solve the problems of ego-motion estimation and moving object detection jointly in a RANSAC paradigm. To accommodate RANSAC to multiple models - a static environment model for ego-motion estimation and a moving object model for moving object detection, a compact representation models moving object information implicitly is proposed. Moving objects are successfully detected without incorporating any grid maps, that are inherently time and space consuming. The experimental results show that accurate identification of static environments can help classification of moving objects, whereas discrimination of moving objects also yields better ego-motion estimation, particularly in environments containing a significant percentage of moving objects.

This paper addresses the problem of laser scanner failure on mirrors and windows. Mirrors and glasses are quite common objects that appear in our daily lives. However, while laser scanners play an important role nowadays in the field of robotics, there are very few literatures that address the related issues such as mirror reflection and glass transparency. We introduce a sensor fusion technique to detect the potential obstacles not seen by laser scanners. A laser-based mirror tracker is also proposed to figure out the mirror locations in the environment. The mirror tracking method is seamlessly integrated with the occupancy grid map representation and the mobile robot localization framework. The proposed approaches have been demonstrated using data from sonar sensors and a laser scanner equipped on the NTU-PAL5 robot. Mirrors and windows, as potential obstacles, are successfully detected and tracked.

Tracking in crowded urban areas is a daunting task. High crowdedness causes challenging data association problems. Different motion patterns from a wide variety of moving objects make motion modeling difficult. Accompanying with traditional motion modeling techniques, this paper introduces a scene interaction model and a neighboring object interaction model to respectively take long-term and short-term interactions between the tracked objects and its surroundings into account. With the use of the interaction models, anomalous activity recognition is accomplished easily. In addition, move-stop hypothesis tracking is applied to deal with move-stop-move maneuvers. All these approaches are seamlessly inter-graded under the variable-structure multiple-model estimation framework. The proposed approaches have been demonstrated using data from a laser scanner mounted on the PALI robot at a crowded intersection. Interacting pedestrians, bicycles, motorcycles, cars and trucks are successfully tracked in difficult situations with occlusion.