EI for AMR Algorithms#

EI for AMR includes reference algorithms as well as deep learning models as working examples of automated robot control functions.

Open Model Zoo for OpenVINO™#

Tutorials: OpenVINO™ Sample Application and ROS 2 OpenVINO™ Toolkit Sample Application

The Open Model Zoo for OpenVINO™ toolkit delivers optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. You can use these pre-trained models instead of training your own models to speed up the development and production deployment process.

For details, see Model Zoo.

ADBSCAN#

Tutorial: ADBSCAN Algorithm

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised clustering algorithm that clusters high dimensional points based on their distribution density. Adaptive DBSCAN (ADBSCAN) has clustering parameters that are adaptive based on range and are especially suitable for processing LIDAR data. It improves the object detection range by 20-30% on average.

Point Cloud Library (PCL)#

Tutorial: Point Cloud Library (PCL) Optimized for the Intel® oneAPI Base Toolkit

The Point Cloud Library (PCL), a standalone, large scale, open project for 2D/3D image and point cloud processing (see also https://pointclouds.org/). The EI for AMR SDK version of PCL adds optimized implementations of several PCL modules which allow you to offload computation to a GPU.

ROS 2 Depth Image to Laser Scan#

ROS 2 Depth Image to Laser Scan, which converts a depth image to a laser scan for use with navigation and localization.

IMU Tools#

Tutorial: Calibrate Your Robot’s Inertial Measurement Unit (IMU) Sensor

IMU Tools - filters and visualizers - from CCNYRoboticsLab/imu_tools:

  • imu_filter_madgwick: A filter which fuses angular velocities, accelerations, and (optionally) magnetic readings from a generic IMU device into an orientation.

  • imu_complementary_filter: A filter which fuses angular velocities, accelerations and (optionally) magnetic readings from a generic IMU device into an orientation quaternion using a novel approach based on a complementary fusion.

  • rviz_imu_plugin: A plugin for rviz which displays sensor_msgs::Imu messages.

FastMapping#

Tutorial: FastMapping Algorithm

FastMapping, which is an algorithm to create a 3D voxel map of a robot’s surrounding, based on Intel® RealSense™ depth sensor data.

Collaborative Visual SLAM#

Tutorial: Collaborative Visual SLAM

Collaborative visual SLAM, a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots. With an edge server maintaining a map database and performing global optimization, each robot can register to an existing map, update the map, or build new maps, all with a unified interface and low computation and memory cost. A collaborative visual SLAM system consists of at least two elements:

  • The tracker is a visual SLAM system with support for inertial and odometry input. It estimates the camera pose in real-time, and maintains a local map. It can work without a server, but if it has one configured, it communicates with the server to query and update the map. The tracker represents a robot. There can be multiple trackers running at the same time.

  • The server maintains the maps and communicates with all trackers. For each new keyframe from a tracker, it detects possible loops, both intra-map and inter-map. Once detected, the server performs map optimization or map merging and distributes the updated map to corresponding trackers.

For collaborative visual SLAM details, refer to A Collaborative Visual SLAM Framework for Service Robots paper.

ROS 2 Cartographer#

Tutorial: 2D LIDAR and ROS 2 Cartographer

ROS 2 Cartographer, a system that provides real-time simultaneous localization and mapping (SLAM) based on real-time 2D LIDAR sensor data. It is used to generate as-built floor plans in the form of occupancy grids.

RTAB-Map#

Tutorials: Wandering Application in Simulations

RTAB-Map (Real-Time Appearance-Based Mapping), a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. The loop closure detector uses a bag-of-words approach to determinate how likely a new image comes from a previous location or a new location. When a loop closure hypothesis is accepted, a new constraint is added to the map’s graph, then a graph optimizer minimizes the errors in the map. A memory management approach is used to limit the number of locations used for loop closure detection and graph optimization, so that real-time constraints on large-scale environments are always respected. RTAB-Map can be used alone with a handheld Kinect, a stereo camera or a 3D lidar for 6DoF mapping, or on a robot equipped with a laser rangefinder for 3DoF mapping.

SLAM Toolbox#

The SLAM toolbox is a set of tools and capabilities for 2D SLAM built by Steve Macenski that includes the following:

  • Starting, mapping, saving pgm files, and saving maps for 2D SLAM mobile robotics

  • Refining, remapping, or continue mapping a saved (serialized) pose-graph at any time

  • Loading a saved pose-graph continue mapping in a space while also removing extraneous information from newly added scans (life-long mapping)

  • An optimization-based localization mode built on the pose-graph. Optionally run localization mode without a prior map for “LIDAR odometry” mode with local loop closures

  • Synchronous and asynchronous modes of mapping

  • Kinematic map merging (with an elastic graph manipulation merging technique in the works)

  • Plugin-based optimization solvers with an optimized Google* Ceres-based plugin

  • rviz2 plugin for interacting with the tools

  • Graph manipulation tools in rviz2 to manipulate nodes and connections during mapping

  • Map serialization and lossless data storage

  • See also SteveMacenski/slam_toolbox.

ITS Global Path Planner#

Tutorials: ITS Path Planner ROS 2 Navigation Plugin and ITS Path Planner Plugin Customization

Intelligent Sampling and Two-Way Search (ITS) global path planner Robot Operating System 2 (ROS 2) Plugin is a plugin for ROS 2 Navigation package which conducts a path planning search on a roadmap from two directions simultaneously. The main inputs are 2D occupancy grid map, robot position, and the goal position. The occupancy is converted into a roadmap and can be saved for future inquiries. The output is a list of waypoints which constructs the global path. All inputs and outputs are in standard ROS 2 formats. This plugin is a global path planner module which is based on the Intelligent Sampling and Two-Way Search (ITS). Currently, the ITS plugin does not support continuous replanning. To use this plugin, a simple behavior tree with compute path to pose and follow path should be used. The inputs for the ITS planner are global 2d_costmap (nav2_costmap_2d::Costmap2D), start and goal pose (geometry_msgs::msg::PoseStamped). The outputs are 2D waypoints of the path. The ITS planner gets the 2d_costmap and it converts it to either Probabilistic Road Map (PRM) or Deterministic Road Map (DRM). The generated roadmap is saved in a txt file which can be reused for multiple inquiries. Once a roadmap is generated, the ITS conducts a two-way search to find a path from the source to destination. Either the smoothing filter or catmull spline interpolation can be used to create a smooth and continuous path. The generated smooth path is in the form of ROS navigation message type (nav_msgs::msg).

Kudan Visual SLAM#

Tutorial: Kudan Visual SLAM

Kudan Visual SLAM (KdVisual), Kudan’s proprietary visual SLAM software, has been extensively developed and tested for use in commercial settings. Open source and other commercial algorithms struggle in many common use cases and scenarios. Kudan Visual SLAM achieves much faster processing time, higher accuracy, and a more robust results in dynamic situations.

Robot Localization#

Tutorial: Start the UP Xtreme i11 Robotic Kit in Localization Mode

robot_localization (from cra-ros-pkg/robot_localization), a collection of state estimation nodes, each of which is an implementation of a nonlinear state estimator for robots moving in 3D space. It contains two state estimation nodes, ekf_localization_node and ukf_localization_node. In addition, robot_localization provides navsat_transform_node, which aids in the integration of GPS data.