Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

10/08/2014
by   Luigi Nardi, et al.
0

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction 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 parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPUaccelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives.

READ FULL TEXT

page 1

page 3

page 6

research
08/21/2018

SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM

SLAM is becoming a key component of robotics and augmented reality (AR) ...
research
09/15/2015

Comparative Design Space Exploration of Dense and Semi-Dense SLAM

SLAM has matured significantly over the past few years, and is beginning...
research
09/11/2023

A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems

The 3D reconstruction of simultaneous localization and mapping (SLAM) is...
research
11/24/2018

Benchmarking and Comparing Popular Visual SLAM Algorithms

This paper contains the performance analysis and benchmarking of two pop...
research
02/13/2019

A Scalable FPGA-based Architecture for Depth Estimation in SLAM

The current state of the art of Simultaneous Localisation and Mapping, o...
research
07/15/2022

mAPN: Modeling, Analysis, and Exploration of Algorithmic and Parallelism Adaptivity

Using parallel embedded systems these days is increasing. They are getti...
research
05/06/2013

A Computer Vision System for Attention Mapping in SLAM based 3D Models

The study of human factors in the frame of interaction studies has been ...

Please sign up or login with your details

Forgot password? Click here to reset