论文标题
IBiscape：在大规模动态环境中进行多模式大满贯系统评估的模拟基准
IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments
论文作者
论文摘要
高保真大满贯系统的开发过程取决于它们对可靠数据集的验证。为了实现这一目标，我们提出了IBiscape，这是一种模拟基准，其中包括来自异质传感器的数据同步和获取API：Stereo-RGB/DVS，DEPTH，IMU和GPS，以及地面真相场景分割和车辆的自我动机。我们的基准建立在Carla模拟器上，Carla模拟器的后端是虚幻的引擎，呈现出模拟现实世界的高动态风景。此外，我们提供适用于自动驾驶汽车导航的34个多模式数据集，包括用于理解诸如事故等场景评估的方案，以及基于与API集成的动态天气模拟类别的广泛框架质量。我们还将第一个校准目标引入了Carla图，以解决CARLA模拟DVS和RGB摄像机的未知失真参数问题。最后，使用IBiscape序列，我们评估了四个ORB-SLAM3系统（单眼RGB，立体声RGB，立体视觉惯性（SVI）和RGB-D）的性能以及在模拟的大型动态环境中收集的各种序列上的玄武岩视觉惯性（VIO）系统。 关键字：基准，多模式，数据集，探测仪，校准，DVS，SLAM
The development process of high-fidelity SLAM systems depends on their validation upon reliable datasets. Towards this goal, we propose IBISCape, a simulated benchmark that includes data synchronization and acquisition APIs for telemetry from heterogeneous sensors: stereo-RGB/DVS, Depth, IMU, and GPS, along with the ground truth scene segmentation and vehicle ego-motion. Our benchmark is built upon the CARLA simulator, whose back-end is the Unreal Engine rendering a high dynamic scenery simulating the real world. Moreover, we offer 34 multi-modal datasets suitable for autonomous vehicles navigation, including scenarios for scene understanding evaluation like accidents, along with a wide range of frame quality based on a dynamic weather simulation class integrated with our APIs. We also introduce the first calibration targets to CARLA maps to solve the unknown distortion parameters problem of CARLA simulated DVS and RGB cameras. Finally, using IBISCape sequences, we evaluate four ORB-SLAM3 systems (monocular RGB, stereo RGB, Stereo Visual Inertial (SVI), and RGB-D) performance and BASALT Visual-Inertial Odometry (VIO) system on various sequences collected in simulated large-scale dynamic environments. Keywords: benchmark, multi-modal, datasets, Odometry, Calibration, DVS, SLAM
