SLAM Depth Modeling via 3D Region Segmentation
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Solution Overview
Problem
Conventional SLAM frameworks face challenges due to high computational costs, noise susceptibility, and failure to leverage relationships between neighboring 3D points, leading to inefficient mapping and localization in resource-constrained devices.
Innovation Solution
A SLAM framework that uses 3D regions to represent the environment map and models depth using planar models, segmenting surfaces in color and depth images and modeling depth values with low-order polynomials, reducing the optimization problem size and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SLAM frameworks use matching keypoints identified across a sequence of camera frames to solve for the robot's pose and environment map, then the mapping and localization can be achieved, but the optimization problem has thousands of variables and requires significant compute resources
Solution Approach 1:
The patent segments the environment map into discrete 3D regions instead of treating all 3D points individually. Each region is represented by a small number of parameters (center coordinates and optional orientation), reducing the optimization variables from thousands of individual point coordinates to a manageable number of region parameters. This segmentation approach maintains mapping accuracy while dramatically reducing computational complexity.
Solution Approach 2:
The patent changes the parameter representation from individual 3D point coordinates (x, y, z for each point) to region-level parameters (center coordinates and orientation). This parameter transformation reduces the dimensionality of the optimization problem from thousands of variables to a small number of region parameters, making the computation feasible for resource-constrained devices while preserving the essential spatial information needed for accurate mapping and localization.
2Reliability
If conventional SLAM frameworks process each 3D point independently in bundle adjustment, then the optimization can be performed, but the computational cost is high and the correlation between neighboring points is not used
Solution Approach 1:
The patent merges neighboring 3D points into discrete 3D regions, treating correlated points as a single entity. By combining multiple individual point estimations into a unified region representation, the system leverages the correlation between neighboring points to improve estimation accuracy while reducing the number of independent optimization variables, thereby increasing processing efficiency.
3Loss of information
If the environment is represented using distinct 3D points, then the mapping can be constructed, but the representation does not leverage the structure of scenes where neighboring points belong to the same object or surface
Solution Approach 1:
The patent segments the set of 3D points into meaningful 3D regions that correspond to objects or surfaces in the scene. This segmentation preserves spatial relationship information by grouping neighboring points that belong to the same physical entity, while the discrete region representation simplifies the data structure compared to managing individual point relationships.
Data Source
AI summary
Embodiments of localization and mapping using depth modeling are described herein. In one example, frames of image data captured by sensor(s) from various poses within an environment are received over an interface. Keypoints are detected in the current frame, and matching keypoints are found in preceding frames. The pose of the current frame is determined based at least partially on depth models associated with the matching keypoints.