ViPlanner: Visual Semantic Imperative Learning for Local Navigation (2310.00982v3)

Abstract: Real-time path planning in outdoor environments still challenges modern robotic systems due to differences in terrain traversability, diverse obstacles, and the necessity for fast decision-making. Established approaches have primarily focused on geometric navigation solutions, which work well for structured geometric obstacles but have limitations regarding the semantic interpretation of different terrain types and their affordances. Moreover, these methods fail to identify traversable geometric occurrences, such as stairs. To overcome these issues, we introduce ViPlanner, a learned local path planning approach that generates local plans based on geometric and semantic information. The system is trained using the Imperative Learning paradigm, for which the network weights are optimized end-to-end based on the planning task objective. This optimization uses a differentiable formulation of a semantic costmap, which enables the planner to distinguish between the traversability of different terrains and accurately identify obstacles. The semantic information is represented in 30 classes using an RGB colorspace that can effectively encode the multiple levels of traversability. We show that the planner can adapt to diverse real-world environments without requiring any real-world training. In fact, the planner is trained purely in simulation, enabling a highly scalable training data generation. Experimental results demonstrate resistance to noise, zero-shot sim-to-real transfer, and a decrease of 38.02% in terms of traversability cost compared to purely geometric-based approaches. Code and models are made publicly available: https://github.com/leggedrobotics/viplanner.

• The paper introduces ViPlanner, a local planning system that integrates geometric and semantic information using a differentiable semantic costmap.
• It employs an unsupervised Imperative Learning framework to fuse perception and planning, enabling effective zero-shot sim-to-real transfer.
• Experimental results demonstrate a 38.02% reduction in traversability cost compared to geometric-only approaches, highlighting enhanced navigation performance.

ViPlanner: Visual Semantic Imperative Learning for Local Navigation

The paper investigates the ongoing challenge of real-time path planning in outdoor environments, addressing a critical gap in current robotic systems that often rely solely on geometric navigation solutions. These traditional approaches are limited in their ability to semantically interpret various terrain types, often failing to identify traversable geometric occurrences, such as stairs. The authors introduce ViPlanner, an innovative local path planning framework that integrates geometric and semantic information to generate actionable paths.

ViPlanner is developed using the Imperative Learning paradigm, optimizing network weights end-to-end based on the planning task's objectives. A key innovation is the use of a differentiable semantic costmap, which enhances the system's ability to discern terrain traversability and obstacles. Importantly, the semantic information is encoded in an RGB colorspace with 30 classes, facilitating effective traversability assessments.

The experimental results are noteworthy, showcasing ViPlanner's robustness to noise, capability for zero-shot sim-to-real transfer, and a 38.02% reduction in traversability cost relative to purely geometric approaches. This improvement demonstrates the system's enhanced capability to navigate varied real-world environments, even without real-world training data.

Methodology and Contributions

ViPlanner's architecture incorporates both a perception and a planning network. These networks process depth and semantic images to create a fused feature embedding. The planning network then utilizes this data to predict a sparse key-point-based path alongside a collision probability, enhancing both safety and path efficacy.

The semantic costmap forms a crucial part of the framework. It translates semantic information into tangible cost factors, enabling improved decision-making during navigation. This costmap is exclusively used during training, simplifying inference procedures and maintaining model efficiency.

The paper highlights several core contributions:

1. Semantic-aware Local Planner: A novel unsupervised Imperative Learning approach is applied to develop a semantically-integrated planning system.
2. Zero-shot Sim-to-Real Transfer: The planner effectively transitions from simulation to real-world applications without requiring real-world data, facilitated by comprehensive simulation-based training.
3. Comparative Evaluations: ViPlanner is benchmarked against a purely geometric approach, demonstrating superior performance in both simulated and real-world conditions.
4. Open-source Tools and Models: The authors provide open access to their code and models, enhancing reproducibility and fostering further research.

Implications and Future Directions

ViPlanner's contribution to robotic navigation is substantial, particularly in terms of integrating multiple domains of information for enhanced path planning. The model's ability to discern complex terrains and navigate effectively without additional real-world training data highlights its potential for varied applications, including urban navigation and off-road explorations.

From a theoretical standpoint, ViPlanner enriches the field by demonstrating the practicality of embedding semantic information directly into path optimization processes. Practically, the planner's design promises more adaptable and robust robotic systems capable of handling unpredictable real-world environments.

Future research could delve into refining the semantic costmap, potentially by automating the assignment of cost values to broaden the planner's applicability. Additionally, incorporating memory mechanisms might further enhance temporal consistency and navigation reliability, allowing robots to retain knowledge about the environment, thereby improving decision-making in dynamic scenarios.

In conclusion, ViPlanner offers a significant advancement in local navigation systems, seamlessly integrating semantic and geometric data to foster more intuitive and capable robotic systems.