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What is VeloView?

VeloView is an adaptable open-source platform designed specifically for the real-time visualization and analysis of 3D LiDAR data collected from a range of Velodyne HDL sensor models, including but not limited to the HDL-64E, HDL-32E, VLP-32, VLP-16, Puck, Puck LITE, Puck Hi-Res, Ultra Alpha, Alpha Prime, Velarray, Velabit, and Veladome. This software allows users to visualize live data streams or access previously recorded data stored in .pcap files, in addition to providing the capability to record live streams for later review. Equipped with tools for displaying, selecting, and measuring data from captured points, VeloView supports customizable color maps based on various criteria such as intensity-of-return, time, distance, azimuth, dual return type, and laser ID. Additionally, users can export data in CSV format as XYZ coordinates or take screenshots of the point cloud visualizations for their records. Recent updates have introduced features like support for Velarray sensors, the ability to run multiple sensors at once, and independent transformation of live data for each sensor, significantly broadening its functionality and range of application. The combination of these capabilities makes VeloView an exceptional resource for professionals engaged in 3D LiDAR technology, ultimately enhancing their analytical and visualization tasks. As such, it is increasingly recognized as an essential tool in the toolkit of those who work with cutting-edge LiDAR systems.

What is Foxglove?

Foxglove is an advanced platform tailored for the visualization, observability, and management of data specifically in the fields of robotics and embodied AI, effectively bringing together a variety of extensive and intricate multimodal temporal datasets such as time series, sensor logs, imagery, lidar/point clouds, and geospatial maps into a single cohesive workspace. It allows engineers to adeptly record, import, organize, stream, and visualize both live and archived data from robotic systems through customizable, user-friendly dashboards that include interactive panels for 3D scenes, plots, images, and maps, thus improving insights into robotic perception, cognition, and actions. Moreover, Foxglove enables seamless real-time integration with systems like ROS and ROS 2 via bridges and web sockets, supports cross-platform functionality (available as a desktop application for Linux, Windows, and macOS), and enhances the processes of analysis, debugging, and performance improvement by synchronizing various data sources in both temporal and spatial dimensions. Its intuitive interface and extensive range of features make it an essential resource for both researchers and developers, facilitating a more efficient workflow within the ever-evolving landscape of robotics. Ultimately, the platform is designed to adapt to the fast-paced advancements in technology, ensuring users remain at the forefront of innovation.

Media

Media

Integrations Supported

C++
Google Sheets
Kitware Glance
Microsoft Excel
Python
Rust

Integrations Supported

C++
Google Sheets
Kitware Glance
Microsoft Excel
Python
Rust

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$18 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Kitware

Date Founded

1998

Company Location

United States

Company Website

www.paraview.org/veloview/

Company Facts

Organization Name

Foxglove

Date Founded

2021

Company Location

United States

Company Website

foxglove.dev/

Categories and Features

Categories and Features

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