Terrain-Aided Localization Using Feature-Based Particle Filtering

Kadetotad, Sneha; Vemulapalli, Pramod K.; Brennan, Sean; Lagoa, Constantino

doi:10.1115/dscc2011-6025

Cited by 5 publications

(4 citation statements)

References 14 publications

(22 reference statements)

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“…In particular, our goal is to formalize the intuition provided by recent work in extracting and encoding extrema features [16], [18], [19] for various applications. More specifically, our key contributions are:…”

Section: Main Contributions Of This Workmentioning

confidence: 99%

“…Some well-known and widely used techniques [16], [18], [19], [20] of feature encoding from extrema are shown in Fig. 4.…”

Section: Encodingmentioning

confidence: 99%

“…From a retrieval standpoint, the fundamental novelty of this method of generating features when compared to the above methods is that it is not necessary to use the sliding window method in this case and this leads to a significant computational benefit. The use of extrema as interest points [17] has become increasingly popular for time-series data analysis, as evidenced by its recent applications in financial time-series analysis [16], audio hashing [18], and vehicle localization [19], [20]. Extrema-based features, which are the focus of our work, can offer several practical benefits:…”

mentioning

confidence: 99%

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Robust Extrema Features for Time-Series Data Analysis

Vemulapalli

Monga

Brennan

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

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Section: Main Contributions Of This Workmentioning

confidence: 99%

“…Some well-known and widely used techniques [16], [18], [19], [20] of feature encoding from extrema are shown in Fig. 4.…”

Section: Encodingmentioning

confidence: 99%

mentioning

confidence: 99%

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Robust Extrema Features for Time-Series Data Analysis

Vemulapalli

Monga

Brennan

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Global Localization with pitch data [8,9] has many similarities with respect to the 'preliminary test' problem using accelerations, but there are a number of key challenges that are specific to acceleration data. Pitch is generally easier to use because the pitch plotted against odometry does not change significantly with speed.…”

Section: Introductionmentioning

confidence: 99%

Pattern Matching of In-Vehicle Acceleration Time Series Data

Vemulapalli

Ledva

Brennan

et al. 2012

Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Pred

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This paper presents a novel approach to find patterns in vehicle x-y-z acceleration data for use in prognostics and diagnostics. In this problem, vehicles are assumed to travel on the same routes and often times as a part of convoys but their GPS and other position information has been removed for privacy reasons. The goal of the pattern matching scheme is to identify the route or convoy associations within vehicles by using the acceleration data collected onboard these vehicles. A crucial step in solving this problem is to choose the right feature vector, as raw matching of acceleration signals is inappropriate due to different velocities, driving behaviors, vehicle loading, etc. In this paper, we demonstrate the feasibility of using 'Multi-Scale Extrema Features' for this application. The paper also addresses implementation details to enhance performance for in-vehicle acceleration data, corrupted by different sources of noise. A novel 'Multi-Scale Encoding' method is also proposed for the above feature vector and it leads to a significant improvement in the performance over traditional pattern matching methods. While the main focus of the paper is towards identifying feature vectors that effectively describe in-vehicle acceleration data, the feature vector could potentially be used with acceleration data obtained from other applications.

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