A trustable and accurate ground truth is a key requirement for benchmarking self-localization and mapping algorithms; on the other hand, collection of ground truth is a complex and daunting task, and its validation is a challenging issue. In this paper we propose two techniques for indoor ground truth collection, developed in the framework of the European project RAW S E E D S, which are mutually independent and also independent on the sensors onboard the robot. These techniques are based, respectively, on a network of fixed cameras, and on a network of fixed laser scanners. We show how these systems are implemented and deployed, and, most importantly, we evaluate their performance; moreover, we investigate the possible fusion of their outputs.

Rawseeds ground truth collection systems for indoor self-localization and mapping

CERIANI, SIMONE;FONTANA, GIULIO ANGELO EUGENIO;GIUSTI, ALESSANDRO;MATTEUCCI, MATTEO;MIGLIORE, DAVIDE ANTONIO;TADDEI, PIERLUIGI
2009-01-01

Abstract

A trustable and accurate ground truth is a key requirement for benchmarking self-localization and mapping algorithms; on the other hand, collection of ground truth is a complex and daunting task, and its validation is a challenging issue. In this paper we propose two techniques for indoor ground truth collection, developed in the framework of the European project RAW S E E D S, which are mutually independent and also independent on the sensors onboard the robot. These techniques are based, respectively, on a network of fixed cameras, and on a network of fixed laser scanners. We show how these systems are implemented and deployed, and, most importantly, we evaluate their performance; moreover, we investigate the possible fusion of their outputs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/564658
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