Hybrid SLAM Map Generation for Featureless Object Detection
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Solution Overview
Problem
Simultaneous Localization and Mapping (SLAM) processes struggle to accurately determine the presence of featureless or narrow objects, leading to sparse landmark points and potential collision risks due to errors in object location determination.
Innovation Solution
Incorporating depth cameras to detect objects not visible to SLAM, and associating depth points with landmark points to create a more comprehensive map, ensuring accurate object location representation and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SLAM processes are used to generate navigation maps, then the system can determine robot location and avoid obstacles, but featureless or narrow objects are not accurately detected leading to sparse landmark points and potential collision risks
Solution Approach 1:
The patent combines SLAM landmark points with depth map points to create a hybrid navigation map. The depth camera captures geometric information of all objects including featureless and narrow objects, while SLAM provides semantically rich landmark points. By merging these two data sources and associating depth points with corresponding landmarks, the system achieves both complete object detection and reliable collision avoidance.
Solution Approach 2:
The patent introduces depth maps as an intermediary data structure that bridges the gap between SLAM landmark detection and complete environment modeling. Depth maps serve as a mediator that captures geometric information of objects that SLAM misses, and through association algorithms, this intermediate data is integrated into the navigation map to enhance object detection completeness.
2Loss of information
If depth cameras are added to detect additional objects, then object detection completeness improves, but system complexity increases
Solution Approach 1:
The patent makes the depth camera serve multiple functions: it detects objects for navigation map generation, provides geometric information for collision avoidance, and enables association with SLAM landmarks. By making the depth sensing system multi-functional, the patent avoids adding separate systems for each function, thereby managing complexity while achieving complete object detection.
Solution Approach 2:
The system uses the depth camera data to automatically associate with SLAM landmarks and update the navigation map without requiring manual intervention. The association algorithm self-adjusts by matching depth points with corresponding landmarks based on spatial relationships, allowing the system to service itself and reduce operational complexity.
3Measurement precision
If more landmark points are captured to improve map completeness, then navigation accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary association between depth points and SLAM landmarks during the mapping phase, organizing data relationships in advance. By pre-establishing these associations and structuring the hybrid map data, the system reduces computational load during navigation and path planning, as the data is already organized for efficient querying and processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and completeness of SLAM maps by including depth points, allowing for safer navigation and improved collision avoidance by accurately representing the environment's geometry.
Implementation Method 1
a second set of points indicative of a location of an object within the environment may be determined based on a depth image acquired using a depth camera
Data Source
AI summary
A point cloud or map of an environment is generated by determining sets of landmark points from multiple images, such as through use of a Simultaneous Localization and Mapping (SLAM) algorithm. Images acquired using a depth camera are used to determine depth points indicative of at least one object not represented by the landmark points. A combined map is generated to include both the landmark points and the depth points. The set of depth points is mapped to a corresponding set of landmark points based on proximity of the points, similarity of the camera poses, or times that the images were acquired. The relationship between the depth and landmark points may be determined. When the landmark points are moved, such as to account for error in the SLAM algorithm, the depth points may be moved to a modified location relative to the landmark points so that the relationship remains constant.