Tolerating failures of continuous-valued sensors
ACM Transactions on Computer Systems (TOCS)
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Enhancing data quality in data warehouse environments
Communications of the ACM
Cleaning and Household Robots: A Technology Survey
Autonomous Robots
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
Towards Sensor Database Systems
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Edits - Data Cleansing at the Data Entry to assert semantic Consistency of metric Data
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Unequal weighting for improved positioning in GPS-less sensor networks
EURASIP Journal on Advances in Signal Processing
Representing Data Quality in Sensor Data Streaming Environments
Journal of Data and Information Quality (JDIQ)
Standardization of mobile phone positioning for 3G systems
IEEE Communications Magazine
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We apply a new type of algorithm for sensor data fusion that was originally developed for estimation of business indicators. The origin of the MCMC algorithm SamPro is the consideration of uncertainty in business indicators, such as profit, sales, and cost, which results from measurement errors or forecasting. Furthermore, the SamPro algorithm uses model-based redundancy to generate virtual measurements; it is able to cope with and can reduce uncertainty of metrical data, including different and even nonparametric data distributions. In this paper, we present an adaptation of the algorithm focused on (distributed) sensor measurements. In such scenarios, the information redundancy bases on multi-modal sensors. Those results can be fused directly or after model based transformations. We validate our approach in a localization scenario fusing laser distance measurements, camera images, and on-board odometry to estimate the current position of a mobile robot. For this purpose we utilize sensor models for each sensor, including specific sensor faults and noise behavior, to generate and fuse virtual sensor measurement.