EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments

TL;DR

EnvoDat is a comprehensive, large-scale multisensory dataset designed to improve robotic spatial awareness and semantic reasoning in diverse, challenging environments like tunnels, natural fields, and indoor spaces under various weather and lighting conditions.

Contribution

The paper introduces EnvoDat, a novel multisensory dataset covering underrepresented environments with extensive annotations, supporting benchmarking and development of resilient robotic algorithms.

Findings

EnvoDat contains over 1.9TB of data across 10 modalities.

Provides 89K polygon-based annotations for 82 object and terrain classes.

Supports benchmarking SLAM and multimodal vision models.

Abstract

To ensure the efficiency of robot autonomy under diverse real-world conditions, a high-quality heterogeneous dataset is essential to benchmark the operating algorithms' performance and robustness. Current benchmarks predominantly focus on urban terrains, specifically for on-road autonomous driving, leaving multi-degraded, densely vegetated, dynamic and feature-sparse environments, such as underground tunnels, natural fields, and modern indoor spaces underrepresented. To fill this gap, we introduce EnvoDat, a large-scale, multi-modal dataset collected in diverse environments and conditions, including high illumination, fog, rain, and zero visibility at different times of the day. Overall, EnvoDat contains 26 sequences from 13 scenes, 10 sensing modalities, over 1.9TB of data, and over 89K fine-grained polygon-based annotations for more than 82 object and terrain classes. We post-processed EnvoDat in different formats that support benchmarking SLAM and supervised learning algorithms, and fine-tuning multimodal vision models. With EnvoDat, we contribute to environment-resilient robotic autonomy in areas where the conditions are extremely challenging. The datasets and other relevant resources can be accessed through https://linusnep.github.io/EnvoDat/.