Motion-Prior Visual Odometry for Wide-Baseline Robot Navigation
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Solution Overview
Problem
Existing visual odometry methods for robot navigation in noisy indoor environments suffer from sample inefficiency, embodiment specificity, and dataset specificity, requiring large compute resources and failing to generalize effectively due to wide-baseline settings and noisy sensor inputs.
Innovation Solution
A method and system for motion prior based visual odometry using a Geometric Coarse Pose Estimator (GCPE) and Neural Fine Pose Regression (NFPR) modules to estimate relative robot pose, incorporating action priors and updating goal coordinates for efficient navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learned VO methods are used with RL-based navigation policies, then robust performance is achieved, but sample inefficiency and high compute resource requirements occur
Solution Approach 1:
The VO pipeline is segmented into two distinct modules: a geometric coarse pose estimator that provides initial pose estimates using traditional computer vision methods, and a neural fine pose regressor that refines these estimates using deep learning. This segmentation allows each module to specialize, with the geometric module handling robust initialization and the neural module providing precise refinement, thereby achieving both reliability and sample efficiency.
Solution Approach 2:
The geometric coarse pose estimator performs preliminary pose estimation before the neural fine pose regressor processes the data. By pre-processing the visual odometry problem with a geometric method that provides reasonable initial estimates, the neural network only needs to learn the residual refinement, significantly reducing the sample complexity and compute resources required for training.
2Speed
If wide-baseline VO estimation is used for fast robot motion, then navigation speed is improved, but measurement precision deteriorates due to reduced overlapping region
Solution Approach 1:
The pose estimation process is divided into coarse geometric estimation and fine neural refinement. The geometric module handles the challenging wide-baseline case by providing a robust initial estimate, while the neural module corrects residual errors, ensuring high precision even when the robot moves fast and baseline is wide.
Solution Approach 2:
The geometric coarse pose estimator acts as an intermediary between the wide-baseline image pairs and the neural fine pose regressor. It transforms the difficult wide-baseline problem into a more manageable form by providing initial pose estimates, which the neural network then refines, thereby maintaining precision across all baseline conditions.
3Device complexity
If classical VO methods are used, then computational simplicity is maintained, but adaptability to noisy indoor environments and embodiment specificity increases
Solution Approach 1:
The system merges classical geometric computer vision methods with modern deep learning approaches. The geometric coarse pose estimator provides computational simplicity and interpretability, while the neural fine pose regressor adds adaptability to noisy indoor environments. This combination allows the system to benefit from both the simplicity of classical methods and the robustness of learned methods.
Solution Approach 2:
Different parts of the pipeline have different qualities: the geometric module provides global, interpretable, and computationally efficient estimates, while the neural module provides local, precise refinements. This local quality differentiation allows the system to maintain overall simplicity while achieving high adaptability in specific critical regions.
Data Source
AI summary
Traditional Visual Odometry (VO) systems face challenges in wide-baseline scenarios leading to drift and catastrophic failures in robot navigation and suffer from sample inefficiency during training. Embodiments of present disclosure provide a method and system for motion prior based VO for ground robot navigation. A robot acquires a previous observation at a previous time step, an action prior, and a current observation at a current time step. Using this data, VO module estimates relative pose between previous and current observations. The VO module comprises a Geometric Coarse Pose Estimator module (GCPE) to estimate a coarse relative pose, and Neural Fine Pose Regression model (NFPR) to regress fine pose using the coarse pose and the acquired inputs. Then, using the relative pose, the current goal coordinates are updated. Finally, a navigation policy determines next action to take to reach the goal using updated goal coordinates and the current observation.