Analogously, it might be interesting to compute the SLAM visualizations in real-time on a remote GPU (e. The solution for CUDA-based ML is to simply not use the Mac and instead do it on remote Linuxes (see my post on remote docker hosts for ML. Not only does the GPU cause overheating if you do crazy things such as connecting a second display, and, hence, heat-throttling, but also developing against it is also very not fun. Of course, anything that involves GPUs on macOS is a nightmare. for visualization, things get a bit more complicated. However, once the software also comprises graphical UIs, e. Of course, Docker makes it easy to run libraries for Linux. Since VSLAM libraries are designated for running efficiently on embedded systems, they are generally programmed in C/C++ and designed with just Linux in mind (even though, for example, in version 2, ROS also aims for compatibility with MacOS).Īs a MacBook user, this becomes “interesting”. For my latest research, I am looking into visual SLAM (e.
0 kommentar(er)
