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 Rawseeds, 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. © 2009 Springer Science+Business Media, LLC.

Taddei, P., Sorrenti, D., Rizzi, D., Migliore, D., Matteucci, M., Marzorati, D., et al. (2009). Rawseeds ground truth collection systems for indoor self-localization and mapping. AUTONOMOUS ROBOTS, 27(4), 353-371 [10.1007/s10514-009-9156-5].

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

SORRENTI, DOMENICO GIORGIO;MARZORATI, DANIELE;
2009

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 Rawseeds, 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. © 2009 Springer Science+Business Media, LLC.
Articolo in rivista - Articolo scientifico
Ground truth · Benchmarking · Pose estimation · Mobile robotics
English
2009
27
4
353
371
none
Taddei, P., Sorrenti, D., Rizzi, D., Migliore, D., Matteucci, M., Marzorati, D., et al. (2009). Rawseeds ground truth collection systems for indoor self-localization and mapping. AUTONOMOUS ROBOTS, 27(4), 353-371 [10.1007/s10514-009-9156-5].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/22194
Citazioni
  • Scopus 114
  • ???jsp.display-item.citation.isi??? 84
Social impact