  SLAM has matured significantly over the past few years, and is beginning to
appear in serious commercial products. While new SLAM systems are being
proposed at every conference, evaluation is often restricted to qualitative
visualizations or accuracy estimation against a ground truth. This is due to
the lack of benchmarking methodologies which can holistically and
quantitatively evaluate these systems. Further investigation at the level of
individual kernels and parameter spaces of SLAM pipelines is non-existent,
which is absolutely essential for systems research and integration. We extend
the recently introduced SLAMBench framework to allow comparing two
state-of-the-art SLAM pipelines, namely KinectFusion and LSD-SLAM, along the
metrics of accuracy, energy consumption, and processing frame rate on two
different hardware platforms, namely a desktop and an embedded device. We also
analyze the pipelines at the level of individual kernels and explore their
algorithmic and hardware design spaces for the first time, yielding valuable
insights.
