EnforceNet Monocular Camera Localization in Sparse LiDAR Point Clouds
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Solution Overview
Problem
Current localization methods for autonomous robots, such as autonomous cars, face challenges with high-definition GPS costs and availability, and camera-based localization stability under varying lighting conditions and scale drift, especially in environments like parking garages where LiDAR scans are sparse and expensive.
Innovation Solution
A novel neural network structure, EnforceNet, that combines camera images with LiDAR point clouds to estimate camera pose using depth projections, incorporating a resistor module for state-value prediction and pose regression, enabling efficient localization even in sparse LiDAR environments and varying lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If camera-based localization is used, then cost is reduced and ubiquity is improved, but stability under varying lighting conditions deteriorates
Solution Approach 1:
The patent merges camera-based visual odometry with LiDAR point cloud data processing. The system combines camera images for feature extraction with LiDAR depth information for geometric constraints, creating a hybrid localization approach that leverages the strengths of both modalities while mitigating their individual weaknesses in terms of lighting sensitivity and computational efficiency
2Adaptability or versatility
If visual odometry is used, then localization can be achieved without GPS, but accuracy deteriorates due to scale drift
Solution Approach 1:
The patent introduces LiDAR point cloud depth projections as an intermediary element that bridges camera-based visual odometry and GPS-like precision. The depth projections from LiDAR serve as geometric anchors that constrain the scale drift accumulation in visual odometry, providing metric accuracy without requiring GPS signals
3Measurement precision
If LiDAR localization is used, then localization accuracy is improved, but computational resources required increase significantly
Solution Approach 1:
The patent segments the LiDAR point cloud into depth projections at multiple levels or regions, processing only the relevant portions rather than the entire point cloud. This segmentation approach reduces the computational burden while maintaining localization accuracy by focusing calculations on the most informative depth layers
4Reliability
If depth projections are sampled from LiDAR point cloud, then localization stability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary sampling of depth projections from the LiDAR point cloud before the main localization computation. By pre-processing and selecting representative depth projections in advance, the system reduces the computational time required during actual localization while maintaining the stability benefits of depth-based constraints
Data Source
AI summary
A method of camera localization, comprising, receiving a camera image, receiving a LiDAR point cloud, estimating an initial camera pose for the camera image, sampling an initial set of depth projections within the LiDAR point cloud, measuring a similarity of the initial camera pose to the initial set of depth projections and deriving a subsequent set of depth projections based on the measured similarity.