Yiliang Xu

Localization of multiple unknown transient radio sources using multiple paired mobile robots with limited sensing ranges

We develop a localization method enabling a team of mobile robots to search for multiple unknown transient radio sources. Due to signal source anonymity, short transmission durations, and dynamic transmission patterns, robots cannot treat the radio sources as continuous radio beacons. Moreover, robots do not know the source transmission power and have limited sensing ranges. To cope with these challenges, we pair up robots and develop a sensing model using the signal strength ratio from the paired robots. We formally prove that the sensed conditional joint posterior probability of source locations for the m-robot team can be obtained by combining the pairwise joint posterior probabilities, which can be derived from signal strength ratios. Moreover, we propose a pairwise ridge walking algorithm (PRWA) to coordinate the robot pairs based on the clustering of high probability regions and the minimization of local Shannon entropy. We have implemented and validated the algorithm under hardware-driven simulation.

System and algorithms for an autonomous observatory assisting the search for the Ivory-Billed Woodpecker

Ornithologists, conservationists, and millions of birdwatchers consider the Ivory-Billed Woodpecker (IBWO) the dasiaHoly Grail of Birds.psila There have been hundreds of reports of sightings of this magnificent bird but no conclusive photo has been recorded since 1948. Under our broader research effort to develop networked autonomous ldquoobservatoriesrdquo for natural environments, we have been working with the Cornell University researchers who have led a massive search initiated by eyewitness reports and low resolution video captured in the Cache River National Wildlife Refuge of Arkansas. In this paper we describe the two-camera autonomous observatory system we designed and installed in Arkansas that continuously scans the sky, recording high resolution video of candidate birds. We report on hardware, software, and algorithms based on 15 months of field experiments. Our image processing algorithm combines size filtering, nonparametric motion filtering, and temporal difference filtering to detect flying birds. Initial results suggest that we have met our four design goals: sensitivity, data reduction, accuracy, and robustness. Video segments with several bird species have been conclusively identified, video data has been consistently reduced by 99.9953%, the system is able to capture birds flying at a speed of up to 60km per hour with low false negative rates, and the system has held up to harsh field conditions. For latest updates and samples of video, please see http://www.c-o-n-e.org/acone/.

Cooperative Search of Multiple Unknown Transient Radio Sources Using Multiple Paired Mobile Robots

We develop a localization method to enable a team of mobile robots to search for multiple unknown transient radio sources. Because of signal source anonymity, short transmission durations, and dynamic transmission patterns, robots cannot treat the radio sources as continuous radio beacons. Moreover, robots do not know the source transmission power and have limited sensing ranges. To cope with these challenges, we pair up robots and develop a cooperative sensing model using signal strength ratios from the paired robots. We formally prove that the joint conditional posterior probability of source locations for the m-robot team can be obtained by combining the pairwise joint posterior probabilities, which can be derived from signal strength ratios. Moreover, we propose a pairwise ridge walking algorithm (PRWA) to coordinate the robot pairs based on the clustering of high-probability regions and the minimization of local Shannon entropy. We have implemented and validated the algorithm under both the hardware-driven simulation and physical experiments. Experimental results show that the PRWA-based localization scheme consistently outperforms the other four heuristics.

Systems and algorithms for autonomously simultaneous observation of multiple objects using robotic PTZ cameras assisted by a wide-angle camera

We report an autonomous observation system with multiple pan-tilt-zoom (PTZ) cameras assisted by a fixed wide-angle camera. The wide-angle camera provides large but low resolution coverage and detects and tracks all moving objects in the scene. Based on the output of the wide-angle camera, the system generates spatiotemporal observation requests for each moving object, which are candidates for close-up views using PTZ cameras. Due to the fact that there are usually much more objects than the number of PTZ cameras, the system first assigns a subset of the requests/objects to each PTZ camera. The PTZ cameras then select the parameter settings that best satisfy the assigned competing requests to provide high resolution views of the moving objects. We solve the request assignment and the camera parameter selection problems in real time. The effectiveness of the proposed system is validated in comparison with an existing work using simulation. The simulation results show that in heavy traffic scenarios, our algorithm increases the number of observed objects by over 200%.

Automatic building exterior mapping using multilayer feature graphs

We develop algorithms that can assist robot to perform building exterior mapping, which is important for building energy retrofitting. In this task, a robot needs to identify building facades in its localization and mapping process, which in turn can be used to assist robot navigation. Existing localization and mapping algorithms rely on low level features such as point clouds and line segments and cannot be directly applied to our task. We attack this problem by employing a multiple layer feature graph (MFG), which contains five different features ranging from raw key points to planes and vanishing points in 3D, in an extended Kalman filter (EKF) framework. We analyze how errors are generated and propagated in the MFG construction process, and then apply MFG data as observations for the EKF to map building facades. We have implemented and tested our MFG-EKF method at three different sites. Experimental results show that building facades are successfully constructed in modern urban environments with mean relative errors of plane depth less than 4.66%.

A Low False Negative Filter for Detecting Rare Bird Species From Short Video Segments Using a Probable Observation Data Set-Based EKF Method

We report a new filter to assist the search for rare bird species. Since a rare bird only appears in front of a camera with very low occurrence (e.g., less than ten times per year) for very short duration (e.g., less than a fraction of a second), our algorithm must have a very low false negative rate. We verify the bird body axis information with the known bird flying dynamics from the short video segment. Since a regular extended Kalman filter (EKF) cannot converge due to high measurement error and limited data, we develop a novel probable observation data set (PODS)-based EKF method. The new PODS-EKF searches the measurement error range for all probable observation data that ensures the convergence of the corresponding EKF in short time frame. The algorithm has been extensively tested using both simulated inputs and real video data of four representative bird species. In the physical experiments, our algorithm has been tested on rock pigeons and red-tailed hawks with 119 motion sequences. The area under the ROC curve is 95.0%. During the one-year search of ivory-billed woodpeckers, the system reduces the raw video data of 29.41TB to only 146.7 MB (reduction rate 99.9995%).

An approximation algorithm for the least overlapping p-Frame problem with non-partial coverage for networked robotic cameras

We report our algorithmic development of the p-frame problem that addresses the need of coordinating a set of p networked robotic pan-tilt-zoom cameras for n, (n > p), competing polygonal requests. We assume that the p frames have almost no overlap on the coverage between frames and a request is satisfied only if it is fully covered. We then propose a Resolution Ratio with Non-Partial Coverage (RRNPC) metric to quantify the satisfaction level for a given request with respect to a set of p candidate frames. We propose a lattice-based approximation algorithm to search for the solution that maximizes the overall satisfaction. The algorithm builds on an induction-like approach that finds the relationship between the solution to the (p - 1)-frame problem and the solution to the p-frame problem. For a given approximation bound isin, the algorithm runs in O(n/isin3 +p2/isin6) time. We have implemented the algorithm and experimental results are consistent with our complexity analysis.

An Efficient Online Hierarchical Supervoxel Segmentation Algorithm for Time-critical Applications.

Video segmentation has been used in a variety of computer vision algorithms as a pre-processing step. Despite its wide application, many existing algorithms require preloading all or part of the video and batch processing the frames, which introduces temporal latency and significantly increases memory and computational cost. Other algorithms rely on human specification for segmentation granularity control. In this paper, we propose an online, hierarchical video segmentation algorithm with no latency. The new algorithm leverages a graph-based image segmentation technique and recent advances in dense optical flow. Our contributions include: 1) an efficient, yet effective probabilistic segment label propagation across consecutive frames; 2) a new method for label initialization for the incoming frame; and 3) a temporally consistent hierarchical label merging scheme. We conduct a thorough experimental analysis of our algorithm on a benchmark dataset and compare it with state-of-the-art algorithms. The results indicate that our algorithm achieves comparable or better segmentation accuracy than state-ofthe-art batch-processing algorithms, and outperforms streaming algorithms despite a significantly lower computation cost, which is required for time-critical applications.

Visual programming for mobile robot navigation using high-level landmarks

We propose a visual programming system that allows users to specify navigation tasks for mobile robots using high-level landmarks in a virtual reality (VR) environment constructed from the output of visual simultaneous localization and mapping (vSLAM). The VR environment provides a Google Street View-like interface for users to familiarize themselves with the robots working environment, specify high-level landmarks, and determine task-level motion commands related to each landmark. Our system builds a roadmap by using the pose graph from the vSLAM outputs. Based on the roadmap, the high-level landmarks, and task-level motion commands, our system generates an output path for the robot to accomplish the navigation task. We present data structures, architecture, interface, and algorithms for our system and show that, given ns search-type motion commands, our system generates a path in O(ns(nr lognr+mr)) time, where nr and mr are the number of roadmap nodes and edges, respectively. We have implemented our system and tested it on real world data.

Exact algorithms for non-overlapping 2-frame problem with non-partial coverage for networked robotic cameras

We report our algorithmic development on the 2-frame problem that addresses the need of coordinating two networked robotic pan-tilt-zoom (PTZ) cameras for n, (n > 2), competing rectangular observation requests. We assume the two camera frames have no overlap on their coverage. A request is satisfied only if it is fully covered by a camera frame. The satisfaction level for a given request is quantified by comparing its desirable observation resolution with that of the camera frame which fully covers it. We propose a series of exact algorithms for the solution that maximizes the overall satisfaction. Our algorithms solve the 2-frame problem in O(n2), O(n2m) and O(n3) times for fixed, m discrete and continuous camera resolution levels, respectively. We have implemented all the algorithms and compared them with the existing work.