Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV (2302.14569v2)

Abstract: Exploration of unknown space with an autonomous mobile robot is a well-studied problem. In this work we broaden the scope of exploration, moving beyond the pure geometric goal of uncovering as much free space as possible. We believe that for many practical applications, exploration should be contextualised with semantic and object-level understanding of the environment for task-specific exploration. Here, we study the task of both finding specific objects in unknown space as well as reconstructing them to a target level of detail. We therefore extend our environment reconstruction to not only consist of a background map, but also object-level and semantically fused submaps. Importantly, we adapt our previous objective function of uncovering as much free space as possible in as little time as possible with two additional elements: first, we require a maximum observation distance of background surfaces to ensure target objects are not missed by image-based detectors because they are too small to be detected. Second, we require an even smaller maximum distance to the found objects in order to reconstruct them with the desired accuracy. We further created a Micro Aerial Vehicle (MAV) semantic exploration simulator based on Habitat in order to quantitatively demonstrate how our framework can be used to efficiently find specific objects as part of exploration. Finally, we showcase this capability can be deployed in real-world scenes involving our drone equipped with an Intel RealSense D455 RGB-D camera.

• The paper presents a mapping pipeline that integrates geometric and semantic data to achieve both expansive exploration and detailed object reconstruction.
• It introduces an innovative utility function that prioritizes object detection by combining map entropy, observed distances, and reconstruction opportunities.
• Experimental results in simulation and real-world tests demonstrate significant improvements in rapid object identification and mapping accuracy over classical methods.

Semantic Object-Centric Exploration with an MAV

The paper "Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV" addresses the challenge of expanding traditional exploration algorithms from a purely geometric standpoint to one that integrates semantic understanding and object-level detail. This paper leverages the capabilities of Micro Aerial Vehicles (MAVs) and proposes a novel approach to exploration that emphasizes not just the uncovering of unknown space, but also the identification and high-quality reconstruction of objects within that space.

Key Contributions

The research presented in this paper offers several significant contributions:

1. Mapping Pipeline: The authors present a mapping pipeline optimized for exploration, path planning, and object reconstruction. By implementing both background and object-level mapping, this pipeline provides a comprehensive understanding of the environment.
2. Exploration Utility Function: An innovative utility function is proposed to prioritize discovering objects and achieving high-quality reconstructions. This function enhances decision-making by weighing factors such as map entropy, observed distances, and detection opportunities.
3. Handling Incomplete Data: A scheme is introduced to mitigate the impact of incomplete depth maps, common in real-world scenarios, by preventing the exploration algorithm from becoming trapped in areas lacking sufficient depth data.
4. Simulation Framework: An open-source MAV simulator integrates Habitat and ROS to facilitate simulated experiments, demonstrating the framework's effectiveness in identifying and reconstructing objects during exploration tasks.

Numerical Results

The experiments conducted, both simulated and real-world, demonstrate that the proposed semantic exploration framework achieves competitive results in terms of explored volume while significantly outperforming classical exploration methods in the detection and reconstruction of objects. The framework not only identifies objects more rapidly but also ensures the acquisition of high-resolution object data, crucial for applications requiring meticulous environment interactions.

Implications and Future Work

The implications of this work are twofold:

1. Practical Applications: By integrating semantic object-level exploration, MAVs can be utilized in environments where understanding and interacting with specific objects is essential—such as in search and rescue operations, warehouse logistics, and automated inventory management.
2. Theoretical Advancements: The introduction of this task-specific exploration method paves the way for further developments in SLAM and planning algorithms, where semantic understanding and object-driven decisions play crucial roles.

Going forward, several areas could benefit from this research:

• Enhanced Object Tracking: Extending the framework to manage dynamic environments with moving objects could provide substantial advantages in domains like autonomous delivery systems.
• Collaborative Exploration: Implementing multi-agent systems based on this framework might lead to optimized exploration and reconstruction in larger and more complex environments.
• Machine Learning Integration: Employing machine learning techniques to predict and locate undiscovered objects based on the partially mapped environment could increase exploration efficiency and accuracy.

In summary, this paper presents a comprehensive and efficient approach to object-centric exploration that holds promise for future advancements in both the practical deployment of autonomous systems and foundational research into robotic exploration methods.