Bundle adjustment (BA) is a fundamental optimization technique used in many crucial applications, including 3D scene reconstruction, robotic localization, camera calibration, autonomous driving, space exploration, street view map generation etc. Essentially, BA is a joint non-linear optimization problem, and one which can consume a significant amount of time and power, especially for large optimization problems. Previous approaches of optimizing BA performance heavily rely on parallel processing or distributed computing, which trade higher power consumption for higher performance. In this paper we propose -BA, the first hardware-software co-designed BA engine on an embedded FPGA-SoC that exploits custom hardware for higher performance and power efficiency. Specifically, based on our key observation that not all points appear on all images in a BA problem, we designed and implemented a Co-Observation Optimization technique to accelerate BA operations with optimized usage of memory and computation resources. Experimental results confirm that -BA outperforms the existing software implementations in terms of performance and power consumption.
Distributed computing is another effective way to optimize BA performance. Eriksson proposed a consensus framework to deal with large scale bundle adjustment in distributed system @cite_10 . Instead of merging small problems by the optimization of overlapping regions of small problems, the consensus framework utilizes the proximal splitting method to formulate the bundle adjustment problem, in which the small problems are merged by averaging points in fact, decreasing the cost of merging. The merging process for the same parameters guarantees the consensus of points in different nodes. This design may suffer from several problems. Firstly, in each iteration, each node in the distributed system has to broadcast all overlapping points to the master node to complete the merging process, which is a huge overhead for large scale data-sets. Secondly, parameters of each camera are independent of parameters of other cameras. However, in practice, some cameras may share the same intrinsic parameters. Thirdly, the method by merging points converges a little slowly in very large scale data-sets and may converge in a local minimum early.
@cite_10
2803297720 2024131347 2059283618 2040176268
The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we derive the distributed formulation from the classical optimization algorithm ADMM, Alternating Direction Method of Multipliers, based on the global camera consensus. Then, we analyze the conditions under which the convergence of this distributed optimization would be guaranteed. In particular, we adopt over-relaxation and self-adaption schemes to improve the convergence rate. After that, we propose to split the large scale camera-point visibility graph in order to reduce the communication overheads of the distributed computing. The experiments on both public large scale SfM data-sets and our very large scale aerial photo sets demonstrate that the proposed distributed method clearly outperforms the state-of-the-art method in efficiency and accuracy. In this paper, we present a hierarchical smart resource coordination and reconfiguration framework for distributed systems. We view the coordination problem as one of context aware resource reconfiguration. The fundamental unit in this hierarchy is a Fault Containment Unit (FCU) that provides run-time fault-tolerance by deciding on the best alternative course of action when a failure occurs. FCUs are composed hierarchically and are responsible for dynamically reconfiguring failing FCUs at lower levels. When such a reconfiguration is not possible, FCUs propagate the failure upward for resolution. We evaluate the effectiveness of our framework in a people tracking application using a network of cameras. The task for our multi-camera network is to allocate pairs of cameras that localize a subject optimally given the current run-time context. The system automatically derives policies for switching between camera pairs that enable robust tracking while being attentive to certain performance measures. Our approach is unique in that we model the dynamics in the scene and the camera network configuration steers the policies to provide robust tracking. One of the fundamental requirements for wide-area visual surveillance using non-overlapping camera networks is the correct association of camera's observations with the tracks of objects under tracking, which is often referred to as data association problem. Most of the current systems work in a centralized manner in that the observations, or the extracted features, on all cameras need to be transmitted to a central server where some association algorithm is running. However, distributed data association scheme, which involves only local information processing on each camera node and information exchanges between neighboring cameras, is more preferable due to a number of well known reasons such as scalability of the system, robustness against single point of failure, and efficiency in use of communication bandwidth, etc. In this paper, we formulate the data association problem as finding the optimal labeling on a factor graph model, and solve the optimization problem under the framework of Dual Decomposition, which can be implemented in a distributed manner. We show the equivalence of the problem of minimizing the energy of the factor graph and that of finding optimal partition of the set of all observations into several subsets, such that all observations belonging to each subset are believed to come from a single object. And the global solution of the dual relaxation of the energy minimization problem is guaranteed to be found by using Dual Decomposition. The effectiveness of the proposed method is demonstrated by comparison with state of the art data association algorithms on real datasets collected by the ten networked cameras mounted in our campus garden. In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth power constraints or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.