Robot Localization Failure Prediction Dataset

This repository contains a synthetic dataset designed for training and evaluating predictive localization monitoring models for autonomous mobile robots, specifically focusing on LiDAR-based particle filter localization (Adaptive Monte Carlo Localization, AMCL). The dataset is generated using NVIDIA Isaac Sim and includes 21 ROS 2 rosbags, capturing diverse scenarios with localization estimates, ground-truth poses, sensor data (LiDAR and odometry), and automatically labeled failure cases. The dataset is intended to support research in proactive fault detection for ground robots navigating in dynamic and challenging environments, as described in the paper "Synthetic Datasets for Data-Driven Localization Monitoring". The dataset comprises 417,185 labeled samples across 21 experiment runs, with a failure rate of 23.1% (96,315 failure instances and 320,870 nominal instances). Experiments were conducted in seven distinct environments: - Warehouse: A large, prebuilt environment with aisles, storage racks, and handling equipment, mimicking real-world warehouse settings. - Symmetric Maps (1–3): Three small-scale environments with symmetrical layouts to induce localization confusion due to repetitive structures. - Asymmetric Maps (1–3): Three small-scale environments with asymmetrical layouts for varied localization challenges. Each environment was tested under three obstacle configurations: - Dynamic Only: 25 spherical obstacles (representing humans, robots, or industrial trucks) with randomized trajectories. - Static Only: Manually placed static obstacles (cubes) not included in the navigational map. - Combined (Dynamic + Static): Both dynamic and static obstacles.