This letter presents a multi-scenario adaptable intelligent robot simulation platform based on LIDAR-inertial fusion, with three main features: (1 The platform includes an versatile robot model that can be freely controlled through manual control or autonomous tracking. This model is equipped with various types of LIDAR and Inertial Measurement Unit (IMU), providing ground truth information with absolute accuracy. (2 The platform provides a collection of simulation environments with diverse characteristic information and supports developers in customizing and modifying environments according to their needs. (3 The platform supports evaluation of localization performance for SLAM frameworks. Ground truth with absolute accuracy eliminates the inherent errors of global positioning sensors present in real experiments, facilitating detailed analysis and evaluation of the algorithms. By utilizing the simulation platform, developers can overcome the limitations of real environments and datasets, enabling fine-grained analysis and evaluation of mainstream SLAM algorithms in various environments. Experiments conducted in different environments and with different LIDARs demonstrate the wide applicability and practicality of our simulation platform. The implementation of the simulation platform is open-sourced on Github.

翻译：本文提出了一种基于激光雷达-惯性融合的多场景自适应智能机器人仿真平台，该平台具备三大主要特征：（1）平台包含一个通用机器人模型，可通过手动控制或自主跟踪进行自由操控。该模型配备了多种类型的激光雷达与惯性测量单元，可提供具有绝对精度的真值信息。（2）平台提供了一系列具有多样化特征信息的仿真环境，并支持开发者根据需求自定义和修改环境。（3）平台支持对SLAM框架的定位性能进行评估。绝对精度的真值信息消除了真实实验中全球定位传感器固有的误差，便于对算法进行细致分析与评估。通过使用该仿真平台，开发者能够突破真实环境与数据集的限制，在各种环境中对主流SLAM算法进行精细化分析与评估。在不同环境及不同激光雷达配置下开展的实验验证了本仿真平台的广泛适用性与实用性。该仿真平台的实现代码已在Github上开源。