TIAND-SLAM: A Multi-Modal SLAM Dataset for Autonomous Navigation

Simultaneous Localization and Mapping (SLAM) is fundamental to autonomous navigation, relying on multimodal dataset to generate high-definition (HD) maps for accurate localization and path planning. However, most existing SLAM datasets primarily feature structured environments with abundant landmarks, such as urban areas with buildings and well-defined road infrastructures. In contrast, highways in semi-urban areas pose significant challenges due to the limited availability of prominent features. To bridge this gap, we introduce TIAND-SLAM (TiHAN-IIT Hyderabad Autonomous Navigation Dataset), a novel multi-modal dataset collected from highway roads, both under and above a flyover in Hyderabad, India, as well as the campus data within the IIT Hyderabad (IITH) campus and TiHAN testbed. TIAND-SLAM is designed to facilitate research on SLAM generalization in feature-scarce environments too. The dataset includes 30 trajectories ranging from 50 meters to 2.5 kilometers, recorded using a sensor suite comprising a LiDAR, six cameras, radar, RTK-GNSS, and an Inertial Measurement Unit (IMU) sensor. Ground truth localization is obtained using RTK-GNSS ensuring precise benchmarking. Additionally, we evaluate SLAM performance by generating maps using LiDAR data. TIAND-SLAM serves as a valuable resource for advancing SLAM research in highway and unstructured terrains, promoting robustness and adaptability in real-world autonomous navigation scenarios.