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Runtime. DoP represents the number of nodes that a node is hidden in. Hence to reflect the privacy level of true states, DoP of true states was computed.
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Runtime. Figure 4.9 shows the runtime report on the two datasets. We can see that the runtime of DPHMM is a little bit longer than DPLS. The reason is that DPLS uses a tighter constraint than Ct, which in our setting became numerous when Markov model converged to a stationary distribution gradually. Then the computation of sensitivity hull took more time with larger graph. It is also worth noting that sensitivity hull converges with Ct. As time evolves, the runtime also converges with Markov model to a stable level.
Runtime. ∼ Our mapping and localization approaches both run online, onboard of a mobile platform. On our robots, we use an Intel NUC10i7FNK and a NVidia Jetson Xavier AGX. The 3D object detection runs at 9FPS on the NVidia Jetson, and the sensor model executes at60FPS on the NUC10i7FNK. This performance is sufficient to construct a 3D metric semantic map and globally localize on our mobile platform. A longer video, including live demoes for both mapping and localization, and the code can be found on our GitHub repository at xxxxx://xxxxxx.xxx/PRBonn/SIMP.
Runtime. We evaluate the runtime performance of our approach in support of our fourth claim, that we are able to operate onboard and allow real-time localization. We tested our approach on a Dell Precision-3640-Tower (with NVidia GeForce RTX 2080) and once on an Intel NUC10i7FNK, which we have on our robot. The Dell PC has 64 GB of RAM and runs at 3.70 GHz. The Intel NUC has 16 GB of RAM and runs at 1.10 GHz. The measurements are reported in Tab. VII. Since we are using 4 cameras simultaneously for object detection, we used an optimized ONNX export of YOLOv5s, and run inference on 320 by 240 images. Qualitative online tests indicates that reducing the resolution does not impact the detection accuracy significantly. These runtime results suggest that our approach is suitable for online localization, and utilizes semantic infor- mation without requiring a GPU onboard.
Runtime. The next set of experiments has been conducted to support our fourth claim that our approach runs fast enough to execute online on the robot in real-time. We, therefore, tested our approach once using a Dell Precision-3640-Tower and once on an Intel NUC10i7FNK, which we have on our YouBot. The Dell PC has 20 CPU cores at 3.70 GHz and 64 GB of RAM. The Intel NUC has 12 CPU cores at
