Semantic-Aware Next-Best-View Planning for Efficient Visual Perception of Specified Objects in Complex Environments using Multi-DoF Mobile Systems

核心概念

The proposed semantic-aware Next-Best-View (NBV) planning framework integrates both visibility gain and semantic gain to efficiently perceive specified objects of interest in complex environments using a multi-degree-of-freedom (multi-DoF) mobile system.

要約

The paper presents a semantic-aware Next-Best-View (NBV) planning framework for efficient visual perception of specified objects in complex environments using a multi-DoF mobile system. The key highlights are:

Formulation of a novel information gain that integrates both visibility gain and semantic gain in a unified form to select the semantic-aware Next-Best-View.
Design of an adaptive strategy with a termination criterion to support a two-stage search-and-acquisition maneuver on multiple objects of interest.
Introduction of semantically relevant reconstruction metrics, including perspective directivity and region of interest (ROI)-to-full reconstruction volume ratio, to evaluate the performance of the proposed approach.
Simulation experiments demonstrating the advantages of the proposed approach over existing methods, achieving improvements of up to 27.13% for the ROI-to-full reconstruction volume ratio and a 0.88234 average perspective directivity.
The planned motion trajectory exhibits better perceiving coverage toward the target compared to existing methods.

統計

The simulation experiments were conducted on three different settings:

Collapsed Room Scene: a 10 m × 10 m × 2.5 m map with various furniture, industrial tools and a standing person within the obstacles.
Kitchen and Dining Room Scene: a 16 m × 10 m × 3.5 m map with common facilities in a family house and a standing person in the corner.
Kitchen and Dining Room with Multiple Specified Objects: the same environment as the Kitchen and Dining Room Scene, but the refrigerator and sink are highlighted as the specific targets.

引用

"Semantics, providing a higher-level interpretation of perception, should significantly contribute to the selection of the next viewpoint for various perception tasks."
"The proposed approach planned more views which have directivity in the range of [-1.0, 0.5) in the Kitchen and Dining scene because it takes more views to search for the target."

抽出されたキーインサイト

Semantic-aware Next-Best-View for Multi-DoFs Mobile System in Search-and-Acquisition based Visual Perception

by Xiaotong Yu,... 場所 arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16507.pdf

深掘り質問

How can the proposed semantic-aware NBV framework be extended to handle dynamic environments or moving objects of interest

To extend the proposed semantic-aware NBV framework to handle dynamic environments or moving objects of interest, several modifications and enhancements can be implemented:

Dynamic Object Tracking: Incorporate object tracking algorithms to continuously update the position and characteristics of moving objects in the environment. This information can then be used to adapt the planning and acquisition process in real-time.

Predictive Modeling: Integrate predictive modeling techniques to anticipate the future positions and behaviors of dynamic objects. By forecasting the movements of objects of interest, the system can proactively plan the next best views to capture relevant information.

Adaptive Planning: Develop adaptive planning strategies that can adjust the viewpoint selection based on the changing dynamics of the environment. This may involve reevaluating the semantic gain and visibility gain metrics dynamically to prioritize views that provide the most valuable information.

Collaborative Perception: Enable collaboration between multiple mobile systems or sensors to collectively track and perceive dynamic objects. By sharing information and coordinating actions, the system can enhance its ability to capture and understand moving targets effectively.

Feedback Loop: Implement a feedback loop mechanism that allows the system to learn from previous observations and refine its planning strategies over time. By continuously updating its knowledge base, the system can improve its performance in dynamic scenarios.

What are the potential challenges and limitations of the current approach in scaling to larger and more complex environments

Scaling the current semantic-aware NBV framework to larger and more complex environments may face several challenges and limitations:

Computational Complexity: As the environment size increases, the computational demands of planning and perception also grow. Handling large-scale environments with intricate structures may require significant computational resources and efficient algorithms to maintain real-time performance.

Semantic Understanding: In more complex environments, the diversity and complexity of semantic information may pose challenges in accurately interpreting and utilizing the semantic gain metric. Ensuring robust semantic understanding across a wide range of objects and contexts is crucial for effective decision-making.

Multi-Object Interactions: Dealing with multiple moving objects or dynamic elements in the environment introduces complexities in planning and coordination. The system must be able to prioritize and track multiple objects simultaneously while optimizing the acquisition process.

Environmental Variability: Larger environments often exhibit greater variability in lighting conditions, occlusions, and object interactions. Adapting the semantic-aware NBV framework to handle such variability and uncertainty is essential for robust performance.

Scalability: Ensuring scalability of the system architecture and algorithms to accommodate the increased complexity and size of the environment is a key consideration. Efficient data management, communication, and coordination mechanisms are vital for scaling up the framework.

How can the semantic information be further leveraged to guide the exploration and acquisition process, beyond the current formulation of semantic gain

To further leverage semantic information in guiding the exploration and acquisition process beyond the current formulation of semantic gain, the following strategies can be considered:

Contextual Understanding: Incorporate contextual information and scene understanding to enhance the semantic-aware NBV framework. By analyzing the relationships between objects, scenes, and contexts, the system can make more informed decisions in viewpoint selection.

Hierarchical Semantic Analysis: Implement hierarchical semantic analysis to capture multi-level semantics and relationships within the environment. By considering semantic hierarchies and dependencies, the system can prioritize views that contribute to a deeper understanding of the scene.

Semantic Fusion: Integrate multiple sources of semantic information, such as object attributes, relationships, and contextual cues, to enrich the semantic-aware decision-making process. By fusing diverse semantic data, the system can gain a more comprehensive understanding of the environment.

Semantic Path Planning: Develop semantic-aware path planning algorithms that consider semantic constraints and objectives during trajectory generation. By incorporating semantic preferences and constraints into the planning process, the system can navigate the environment more effectively.

Interactive Exploration: Enable interactive exploration modes where users can provide semantic cues or feedback to guide the exploration process. By incorporating human input and feedback, the system can adapt its exploration strategy based on user preferences and domain knowledge.