Hierarchical Stereo Navigation with Sparse Representation

Vision-based navigation systems with real-time requirements often employ hierarchical schemes that decompose navigation tasks across multiple temporal and spatial scales. This approach scales the problem down to real-time realizable sub-problems in local domains. Such hierarchical architectures typically consist of environment perception, planning, and control modules, which are arranged across three levels, from high to low. Leveraging sparse representations for these modules can reduce computational costs and offer favorable scalability across multiple devices. This is crucial for mobile robot navigation, where fast and safe decisions must be made when traversing unknown or partially known environments. Different perception strategies create distinct levels of representation for vision-based navigation. Among the passive methods for constructing perception space, this thesis investigates depth estimation using lightweight stereo cameras. Specifically, it explores the use of sparse stereo representations in vision-based navigation. In the middle level, safety is a key concern in vision-based navigation. This thesis proposes gap-based local planning for various robot dynamics to ensure safety. Sparse gaps are detected within any laser scan-like egocentric perception. The low-level module in hierarchical navigation systems is motion control. Different from traditional pose-based control, this thesis describes a stereo image-based visual servoing system for trajectory tracking by nonholonomic robots without externally derived pose information nor a known visual map of the environment. In summary, sparse representation is utilized across three levels—perception, motion planning, and motion control—in hierarchical stereo navigation. Sparse representation improves computational efficiency, safety, and task completion in stereo vision-based navigation systems.

Date

2025-02-19

Resource Type

Text

Resource Subtype

Dissertation

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