Nordic nRF52811, nRF52820, nRF52833, nRF52840 Are also supposed to be able to support AOA on BE 5.1 but in looking tough their forums, No one seems to have been able to make it work. There is no dev boards or demo kits even though there are many arduino ecosystem modules using these.
PJSIP is a free and open source multimedia communication library written in C language implementing standard based protocols such as SIP, SDP, RTP, STUN, TURN, and ICE. It combines signaling protocol (SIP) with rich multimedia framework and NAT traversal functionality into high level API that is portable and suitable for almost any type of systems ranging from desktops, embedded systems, to mobile handsets.
PJSIP is both compact and feature rich. It supports audio, video, presence, and instant messaging, and has extensive documentation. PJSIP is very portable. On mobile devices, it abstracts system dependent features and in many cases is able to utilize the native multimedia capabilities of the device.
A page of LiDAR SLAM Navigatio Resources (LiDAR-SLAM-Nav-RES) to follow up current LiDAR SLAM based Navigation trends, including key papers, books, engineering projects, as well as valuable blogs.
(Current) Project III — Motion Planning
ROS Research Papers
ROS Layered Costmaps
David V. Lu, D. Hershberger and W. D. Smart, "Layered costmaps for context-sensitive navigation," 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, 2014, pp. 709-715. pdf
David Vincent Lu, "Contextualized Robot Navigation" (2014). Engineering and Applied Science Theses & Dissertations. 62. pdf, pdf(candidate)
Layered Social Cost Map
Kollmitz, Marina, et al. "Time Dependent Planning on a Layered Social Cost Map for Human-Aware Robot Navigation." 2015 European Conference on Mobile Robots (ECMR). IEEE, 2015. pdf
(2019/07/19) David V. Lu, Daniel B. Allan, and William D. Smart. "Tuning Cost Functions for Social Navigation." International Conference on Social Robotics. Springer, Cham, 2013. pdf
Guimarães R L, de Oliveira A S, Fabro J A, et al. ROS navigation: Concepts and tutorial[M]//Robot Operating System (ROS). Springer, Cham, 2016: 121-160. pdf
Robotics Engineering 2: ROS-Turtlebot Motion Control and Navigation
AK Assad, Mashruf Chowdhury, and Yanik Porto, Robotics Engineering 2: ROS-Turtlebot Motion Control and Navigation. May 11, 2015. pdf
Books
Robotics (Release 1.4)
Jeff McGough, Book title: Robotics. Date: Dec./02/2018. pdf
Project II — Laser-based SLAM (Part 1): Google Cartographer
Google Cartographer Hess W, Kohler D, Rapp H, et al. Real-time loop closure in 2D LIDAR SLAM [C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 1271-1278.
Correlative Scan Matching Olson E B. Real-time correlative scan matching[C]//2009 IEEE International Conference on Robotics and Automation. IEEE, 2009: 4387-4393.
Ceres Scan Matching Kohlbrecher S, Von Stryk O, Meyer J, et al. A flexible and scalable slam system with full 3d motion estimation[C]//2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. IEEE, 2011: 155-160.
Branch and Bound Algorithm Clausen J. Branch and bound algorithms-principles and examples[J]. Department of Computer Science, University of Copenhagen, 1999: 1-30.
Mendes, E., Koch, P., & Lacroix, S. (2016, October). ICP-based pose-graph SLAM. In 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (pp. 195-200). IEEE.
Kümmerle, R., Steder, B., Dornhege, C., Ruhnke, M., Grisetti, G., Stachniss, C., & Kleiner, A. (2009). On measuring the accuracy of SLAM algorithms. Autonomous Robots, 27(4), 387.
The Intel® Movidius™ Myriad™ X VPU is Intel's first VPU to feature the Neural Compute Engine — a dedicated hardware accelerator for deep neural network inference. The Neural Compute Engine in conjunction with the 16 powerful SHAVE cores and high throughput intelligent memory fabric makes Movidius Myriad X ideal for on-device deep neural networks and computer vision applications.
The Movidius Myriad X VPU is programmable with the Intel® Distribution of the OpenVINO™ toolkit for porting neural network to the edge, and via the Myriad Development Kit (MDK) which includes all necessary development tools, frameworks and APIs to implement custom vision, imaging and deep neural network workloads on the chip.
Open Source Spatial AI From The Biggest Name in Computer Vision.
To celebrate OpenCV’s 20th anniversary we are proud to introduce the OpenCV AI Kit (OAK), an MIT-licensed open source software and Myriad X-based hardware solution for computer vision at any scale.
OAK consists of the OAK API software and two different types of hardware: OAK-1 and OAK-D. They are tiny artificial intelligence (AI) and computer vision (CV) powerhouses, with OAK-D providing spatial AI leveraging stereo depth in addition to the 4K/30 12MP camera that both models share. They are also both absurdly easy to use. Up and running in under 30 seconds, OAK-1 and OAK-D allow anyone to access this power: hobbyists, researchers, and professionals alike. Once you're done tinkering, OAK's modular, FCC/CE-approved, open-source hardware ecosystem affords direct integration into your products.
