DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds (2003.03464v1)

Abstract: Planning in unstructured environments is challenging -- it relies on sensing, perception, scene reconstruction, and reasoning about various uncertainties. We propose DeepSemanticHPPC, a novel uncertainty-aware hypothesis-based planner for unstructured environments. Our algorithmic pipeline consists of: a deep Bayesian neural network which segments surfaces with uncertainty estimates; a flexible point cloud scene representation; a next-best-view planner which minimizes the uncertainty of scene semantics using sparse visual measurements; and a hypothesis-based path planner that proposes multiple kinematically feasible paths with evolving safety confidences given next-best-view measurements. Our pipeline iteratively decreases semantic uncertainty along planned paths, filtering out unsafe paths with high confidence. We show that our framework plans safe paths in real-world environments where existing path planners typically fail.

• The paper introduces a hypothesis-based planning framework that integrates Bayesian neural networks with semantic point clouds to assess terrain safety.
• It employs a multi-stage pipeline, including a Next-Best-View planner, to iteratively reduce semantic uncertainty and evaluate multiple trajectory options.
• Experimental results demonstrate significant improvements in safe path selection in complex environments, underscoring the framework's robustness and real-world applicability.

DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds

This paper presents DeepSemanticHPPC, an innovative approach to path planning in unstructured outdoor environments by addressing the uncertainty inherent in semantic representations of terrain. The work leverages a combination of Bayesian neural networks (BNN), semantic point cloud representations, and hypothesis-driven path planning to effectively identify safe paths that minimize risk and uncertainty.

Algorithmic Overview

The framework rests on a multi-stage pipeline consisting of several core components:

1. Deep Bayesian Neural Network: Utilized for estimating semantic surfaces with associated uncertainty, the BNN encodes rich, contextual scene understanding that informs path safety assessments.
2. Flexible Point Cloud Representation: The environment is represented as a semantic point cloud, which enables the dynamic and nuanced differentiation of terrain types that are critical for accurate path planning in unstructured outdoor settings.
3. Next-Best-View (NBV) Planner: This module iteratively refines the understanding of the environment by proposing additional viewpoints. It reduces scene semantics' uncertainty, thus enhancing the safety and accuracy of the planned paths.
4. Multi-Hypothesis Path Planner: By proposing multiple kinematically feasible trajectories, this planner evaluates the evolving safety confidence of each path using the refined semantic information, filtering out paths deemed high-risk.

Results and Evaluation

The experimental results demonstrate that DeepSemanticHPPC can substantially outperform traditional path planners, particularly in ambiguous and complex environments where existing techniques inadequately differentiate safe from unsafe paths. Through trials conducted in simulated and real-world scenarios, the proposed method not only reduces the incidence of unsafe path selections but also verifies its ability to halts operations when all paths are deemed unsafe.

Key numerical results underline the utility of incremental improvements integrated into the framework, such as the NBV planner's capacity to iteratively decrease the uncertainty in semantic segmentation, thereby increasing the robustness of path safety assessments. The paper highlights a significant increase in selecting safe paths alongside confirmation rates of paths as either safe or unsafe as a function of the number of iterated views.

Implications and Future Directions

The implications of this research are multi-fold. Practically, it offers a robust framework that enhances robotic autonomy in navigation, particularly for exploratory or surveillance tasks in unstructured environments. Theoretically, it paves the way for integrating probabilistic models with classical robotics algorithms, potentially inspiring improvements in how robots perceive and interact with the real world.

Looking ahead, potential areas for further exploration include the integration of the framework with online map-building capabilities and embedding geometric uncertainties. Additionally, deploying this system on physical robotic platforms will provide further validation of its efficacy and robustness in live environments.

In summary, DeepSemanticHPPC presents a significant advancement in the field of robotics, offering a sophisticated mechanism for navigating challenging terrains with enhanced safety and reliability.