IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments

TL;DR

IBISCape is a comprehensive simulated benchmark built on CARLA that provides multi-modal datasets and APIs for evaluating multi-modal SLAM systems in large-scale dynamic environments, aiding development and validation.

Contribution

We introduce IBISCape, a new simulated benchmark with synchronized multi-modal sensor data, calibration tools, and diverse scenarios for robust SLAM system evaluation.

Findings

Evaluated four ORB-SLAM3 systems and BASALT VIO on IBISCape sequences.

Demonstrated the benchmark's effectiveness for large-scale dynamic environment testing.

Provided calibration solutions for simulated DVS and RGB cameras.

Abstract

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