MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

Indoor navigation has long been a key challenge in robotics, evolving from classical SLAM techniques to deep learning-based approaches. Early methods relied on geometric mapping and localization, but struggled with dynamic and cluttered environments. Recent advances, such as DeepMind's DQN and A3C algorithms, demonstrated success in simulated maze-like environments, yet their performance drops significantly in realistic, large-scale indoor scenes. Platforms like AI2-THOR and DeepMind Lab provided initial support but lacked the realism and multi-sensory integration needed for real-world deployment. MINOS addresses these gaps by combining large datasets, high-fidelity rendering, and multi-modal sensing, enabling more accurate and robust evaluation of navigation algorithms in complex indoor environments.

Despite progress, deep RL methods face significant hurdles in large, cluttered indoor scenes, with success rates remaining low. Challenges include perception noise, occlusion, environment variability, and limited sensor modalities. Existing simulators lack either realism or scalability, hindering effective training and benchmarking. This impairs the development of autonomous agents capable of operating reliably in real-world settings, where environmental complexity and sensor imperfections are unavoidable. Overcoming these issues requires a high-performance, realistic simulation environment supporting diverse sensor configurations and environment variations, to facilitate robust learning and evaluation.

MINOS introduces a scalable, high-speed simulation framework supporting multi-modal perception—vision, depth, normals, touch, and semantics—integrated with large datasets of synthetic and real environments. It enables environment randomization, material variation, and object clutter control, fostering generalization. The platform supports multiple goal types and flexible sensor configurations, allowing comprehensive benchmarking of deep RL algorithms. Its core innovation lies in combining realism, speed, and configurability, providing a standardized, versatile testbed for indoor navigation research, bridging the gap between simulation and real-world deployment.

• �� Data sources: SUNCG and Matterport3D datasets, providing diverse indoor scenes.
• �� Environment setup: scene variation via re-texturing, object removal, and material changes.
• �� Sensor configuration: RGB, depth, normals, contact, semantic segmentation, and measurements, supporting multiple streams.
• �� Task design: PointGoal (spatial coordinates), ObjectGoal (object category), RoomGoal (room type), with success thresholds.
• �� Agent model: cylindrical proxy with continuous control, supporting linear and angular acceleration.
• �� Algorithms: training with A3C, LSTM A3C, UNREAL, and DFP, incorporating environment randomization for robustness.

Experiments involved training and testing agents on subsets of SUNCG and Matterport3D, with environment complexity varying from simple to highly cluttered. Success rates, path efficiency, and robustness to environment variations were measured. Ablation studies assessed the impact of sensor modalities and environment randomization. Cross-dataset generalization was evaluated by training on synthetic scenes and testing on reconstructed real scenes, revealing significant performance gaps. Results highlighted the importance of multi-modal fusion and environment diversity for robust navigation.

In complex Matterport3D environments, the best algorithms achieved only 20% success in PointGoal tasks and 14% in RoomGoal, indicating substantial room for improvement. Multi-modal fusion, especially depth combined with touch, significantly outperformed single-modality approaches. Environment complexity caused notable performance drops, validating the importance of scene diversity. Fusion of sensory inputs proved particularly effective in cluttered scenes, reducing occlusion and noise effects, thus improving robustness and generalization.

MINOS can accelerate development of autonomous indoor robots, virtual assistants, and smart building management systems. Its realistic environment simulation supports algorithm benchmarking, sensor fusion research, and robustness testing. The platform is suitable for academia and industry, enabling rapid prototyping, evaluation, and transfer of navigation solutions to real-world robots, ultimately enhancing indoor automation and assistive technologies.

Despite advances, deep RL algorithms still struggle with success rates below 20% in highly complex scenes, indicating the need for more robust models. The simulation, although realistic, cannot fully replicate real-world sensor noise, dynamics, and environmental variability, which may hinder transferability. High computational costs limit widespread adoption, and current sensor models lack certain real-world complexities such as dynamic obstacles and multi-sensory noise. Further improvements are needed for practical deployment.