bhomaidan1990

bhomaidan1990

Azure Kinect on Ubuntu 18.04

Notes on Installing Microsoft Azure Kinect Sensor and Body Tracking SDKs on Linux PC and NVIDIA Jetson Xavier NX

06.12.2020

Jump to:

• Installing Sensor SDK on Linux PC
• Installing on Jetson Xavier NX
• Installing Body Tracking SDK
• Updating Firmware for Azure Kinect

bhomaidan1990

#include "potentialfield.h"

// CONSTRUCTOR OF POTENTIALFIELD
PotentialField::PotentialField()
{
    rMult = 15.0;
    meanNumber = 8;
    maxPointDiff = 0.05;
}

bhomaidan1990

What is NUMA (Non-Uniformed Memory Access)

Non-Uniform Memory Access (NUMA) is one of the computer memory design methods used in multiprocessor systems, and the time to access the memory varies depending on the relative position between the memory and the processor. In the NUMA architecture, when a processor accesses its local memory, it is faster than when it accesses the remote memory. Remote memory refers to memory that is connected to another processor, and local memory refers to memory that is connected to its own processor. In other words, it is a technology to increase memory access efficiency while using multiple processors on one motherboard. When a specific processor runs out of memory, it monopolizes the bus by itself, so other processors have to play. , and designate 'access only here', and call it a NUMA node.

1. Check Nodes

lspci | grep -i nvidia
  
01:00.0 VGA compatible controller: NVIDIA Corporation TU106 [GeForce RTX 2060 12GB] (rev a1)

bhomaidan1990

//* The Ros object, wrapping a web socket connection to rosbridge.
var ros = new ROSLIB.Ros({
  url: 'ws://localhost:9090' // url to your rosbridge server
});

//* A topic for messaging.
var exampleTopic = new ROSLIB.Topic({
  ros: ros,
  name: '/com/endpoint/example', // use a sensible namespace
  messageType: 'std_msgs/String'

bhomaidan1990

var path = require('path');
var webpack = require('webpack');
var isProduction = process.env.NODE_ENV === 'production';

/*
* Compile for usage in a browser-like environmen or for the server.
*/

exports.target = 'web';

bhomaidan1990

% Copyright (c) 2010 Michael Ummels <michael@ummels.de>
%
% Permission to use, copy, modify, and/or distribute this software for any
% purpose with or without fee is hereby granted, provided that the above
% copyright notice and this permission notice appear in all copies.
%
% THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
% WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
% MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
% ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES

bhomaidan1990

Installing boost libraries for GCC (MinGW) on Windows

Folder setup

1. Extract downloaded boost source, e.g. C:\Program Files\boost_1_59_0.
2. Create a folder for Boost.Build installation, e.g. C:\Program Files\boost-build.
3. Create a folder within for building, i.e. C:\Program Files\boost_1_59_0\build.
4. Create a folder for installation, e.g. C:\Program Files\boost.

GCC setup

1. Open Command Prompt.

bhomaidan1990

cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D WITH_QT=ON \
-D WITH_VTK=OFF \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=ON \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
-D BUILD_EXAMPLES=ON .. \

bhomaidan1990

#!/usr/bin/python3

import sys
from math import pi as PI
import rospy
import tf
from std_msgs.msg import Float64
from geometry_msgs.msg import *
from sensor_msgs.msg import *
from moveit_msgs.msg import *