28,453 research outputs found
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval
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