Hybrid SLAM Registration Using 3D Lines and Points
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Solution Overview
Problem
Conventional SLAM systems using RGB-D sensors are limited by hardware constraints and noise, which result in inaccurate registration due to the exclusion of depth measurements outside a limited range, especially in scenes with large depth variations, and typically rely on a single type of primitive for registration.
Innovation Solution
A hybrid SLAM system that registers 3D points and 3D lines, where keypoints with valid depths are used as 3D features and those without valid depths are represented as 2D features, enabling point-to-point, line-to-point, and line-to-line correspondences to generate longer-range constraints, and also utilizes plane features for improved registration accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only pixels with valid depths are used for registration, then registration accuracy is maintained within limited depth range, but all pixels associated with scene points too close or too far are ignored, introducing registration inaccuracy especially for scenes with large depth variations
Solution Approach 1:
The patent transforms 2D image points into 3D rays originating from the camera center, allowing all 2D points to be utilized regardless of depth validity. By representing measurements as 3D lines/rays instead of requiring valid depth values, the system converts a 2D constraint problem into a 3D geometric problem where depth information is implicitly encoded in the ray direction rather than requiring explicit depth measurements.
Solution Approach 2:
The patent changes the measurement representation from 3D points (requiring valid depth) to 3D lines/rays (using only 2D coordinates). This parameter transformation allows the system to accept all 2D image points as valid measurements, converting the depth validity constraint into a geometric ray definition that works for all points in the image regardless of their depth distance.
2Device complexity
If a single type of primitive (2D or 3D points) is used for measurement, then the system is simple to implement, but it cannot effectively utilize both color and depth data for improved registration accuracy
Solution Approach 1:
The patent merges 2D point measurements from color images with 3D point measurements from depth images into a unified 3D line-based measurement framework. Both 2D and 3D primitives are converted into 3D geometric constraints (rays/lines) that can be jointly processed, combining the advantages of both color and depth data while maintaining a consistent mathematical formulation.
Solution Approach 2:
The patent creates a universal measurement representation (3D lines/rays) that can accommodate both 2D image points and 3D spatial points. This multi-functional framework allows the same registration algorithm to process diverse measurement types uniformly, making the system adaptable to different sensor data types without requiring separate processing pipelines.
3Measurement precision
If 3D sensors are used to increase registration accuracy, then depth information is available, but hardware limitations and noise restrict accurate depths to a limited range only
Solution Approach 1:
The patent introduces 3D lines/rays as an intermediary representation that bridges 2D image measurements and 3D spatial reconstruction. This intermediary allows the system to leverage accurate depth measurements where available while simultaneously utilizing 2D point correspondences where depth is unreliable, with the 3D line serving as a common geometric framework that integrates both measurement types.
Data Source
AI summary
A method and system for registering data by first acquiring the data from a scene by a sensor at different viewpoints, and extracting, from the data, three-dimensional (3D) points and 3D lines, and descriptors associated with the 3D points and the 3D lines. A first set of primitives represented in a first coordinate system of the sensor is selected, wherein the first set of primitives includes at least three 3D points. A second set of primitives represented in a second coordinate system is selected, wherein the second set of primitives includes any combination of 3D points and 3D lines to obtain at least three primitives. Then, using the first set of primitives and the second set of primitives, the 3D points are registered with each other, and the 3D points with the 3D lines to obtain registered primitives, wherein the registered primitives are used in a simultaneous localization and mapping (SLAM) system.