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DTSTART:20141026T030000
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UID:calendar.6978.field_data.0@www.corsodrupal.uniroma1.it
DTSTAMP:20260418T233311Z
CREATED:20141216T115542Z
DESCRIPTION:Real-time dense computer vision and SLAM offer great potential 
 for a new level of scene modelling\, tracking and real environmental inter
 action for many types of robot\, but their high computational requirements
  mean that use on mass market embedded platforms is challenging. Meanwhile
 \, trends in low-cost\, low-power processing are towards massive paralleli
 sm and heterogeneity\, making it difficult for robotics and vision researc
 hers to implement their algorithms in a performance-portable way. In this 
 paper we introduce SLAMBench\, a publicly-available software framework whi
 ch represents a starting point for quantitative\, comparable and validatab
 le experimental research to investigate trade-offs in performance\, accura
 cy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides
  a KinectFusion implementation in C++\, OpenMP\, OpenCL and CUDA\, and har
 nesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory a
 nd scene ground truth for reliable accuracy comparison of different implem
 entation and algorithms. We present an analysis and breakdown of the const
 ituent algorithmic elements of KinectFusion\, and experimentally investiga
 te their execution time on a variety of multicore and GPUaccelerated platf
 orms. For a popular embedded platform\, we also present an analysis of ene
 rgy efficiency for different configuration alternatives
DTSTART;TZID=Europe/Paris:20141219T120000
DTEND;TZID=Europe/Paris:20141219T120000
LAST-MODIFIED:20141216T120313Z
LOCATION:Aula Magna\, DIAG
SUMMARY:Introducing SLAMBench\, a performance and accuracy benchmarking met
 hodology for SLAM - Luigi Nardi (Imperial College\, London)
URL;TYPE=URI:http://www.corsodrupal.uniroma1.it/node/6978
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