Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle

Real-Time Robot Localization, Vision, and Speech Recognition on Nvidia Jetson TX1

Robotics systems are complex, often consisted of basic services including SLAM for localization and mapping, Convolution Neural Networks for scene understanding, and Speech Recognition for user interaction, etc. Meanwhile, robots are mobile and usually have tight energy constraints, integrating these services onto an embedded platform with around 10 W of power consumption is critical to the proliferation of mobile robots. In this paper, we present a case study on integrating real-time localization, vision, and speech recognition services on a mobile SoC, Nvidia Jetson TX1, within about 10 W of…

FeatSense——一种面向NVIDIA Jetson板卡的、带有GPU加速TSDF建图后端的基于特征的配准算法

FeatSense -- A Feature-based Registration Algorithm with GPU-accelerated TSDF-Mapping Backend for NVIDIA Jetson Boards

本文提出面向高分辨率激光雷达的GPU加速SLAM系统FeatSense，搭配嵌入式端实时TSDF建图模块，可在NVIDIA AGX Xavier上实现10Hz的128线激光点云配准，TSDF建图速度较同功耗下的此前工作提升100倍。

Real-time Vision-based Depth Reconstruction with NVidia Jetson

Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution. Reconstructing depth from single image is particularly valuable to mobile robotics as it can be embedded to the modern vision-based simultaneous localization and mapping (vSLAM) methods providing them with the metric information needed to construct accurate maps in real scale. Typically, depth reconstruction is done nowadays via fully-convolutional neural networks (FCNNs). In this work we experiment with several FCNN architectures and introduce a few enhanceme…

面向高速无GPS环境的嵌入式热惯性里程计实时系统

Real-Time Thermal-Inertial Odometry on Embedded Hardware for High-Speed GPS-Denied Flight

该研究提出了一种嵌入式实时热惯性里程计系统，通过融合热成像与惯性数据实现高速无GPS环境下可靠飞行

Toward Efficient and Robust Multiple Camera Visual-inertial Odometry

Efficiency and robustness are the essential criteria for the visual-inertial odometry (VIO) system. To process massive visual data, the high cost on CPU resources and computation latency limits VIO's possibility in integration with other applications. Recently, the powerful embedded GPUs have great potentials to improve the front-end image processing capability. Meanwhile, multi-camera systems can increase the visual constraints for back-end optimization. Inspired by these insights, we incorporate the GPU-enhanced algorithms in the field of VIO and thus propose a new front-end with NVIDIA Visi…

一致且高效的基于MSCKF的无人机激光雷达-惯性里程计与推断的簇到平面约束

Consistent and Efficient MSCKF-based LiDAR-Inertial Odometry with Inferred Cluster-to-Plane Constraints for UAVs

本文提出了一种针对无人机的高效紧密耦合LIO框架，通过推断的平面特征构建共面约束，利用空域投影消除状态向量中的特征参数依赖，提升一致性与效率，实现实时运行于资源受限平台。