LiDAR Pose Estimation Using Neural Implicit Scene Modeling
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Solution Overview
Problem
Existing vehicle sensor systems struggle to accurately compute the pose of lidar sensors in real-time, especially in dynamic environments, without relying on camera input.
Innovation Solution
The use of a neural implicit representation, implemented via a multilayer perceptron, to model the geometry of a scene using lidar measurements, allowing for the computation of the lidar sensor's pose without camera data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera input is used to compute lidar sensor pose, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts the pose computation function from a multi-sensor fusion approach (lidar + camera) to a lidar-only approach. By removing the camera dependency and using only lidar sensor measurements with neural implicit representations, the system achieves pose computation without the added complexity of camera hardware and calibration while maintaining measurement precision through the neural network's ability to model scene geometry from lidar data alone.
2Manufacturing precision
If neural implicit representation is trained with more sampling iterations, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural implicit representation offline with extensive sampling iterations to achieve high scene geometry modeling accuracy. This pre-training phase creates a robust model that can then be deployed in real-time applications with minimal additional computation, effectively shifting the time investment to an offline phase rather than during critical real-time pose computation.
Solution Approach 2:
The patent uses excessive action in the training phase by performing more sampling iterations than strictly necessary for convergence, ensuring the neural implicit representation achieves high precision for complex scenes. This excessive training effort is acceptable because it occurs offline, and the resulting model provides sufficient accuracy for real-time deployment with acceptable computation times.
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to generate a set of points from a measurement scan obtained by a lidar sensor and to generate an expected termination distance of the set of points based on a neural implicit representation of the set of points. The instructions may additionally be to compute a loss function that includes a relatively low margin correlated with the variance or standard deviation of a training distribution centered at a learned point of the set of points based on the expected termination distance of the learned point, the learned point being learned by the neural implicit representation. The instructions may additionally be to generate a keyframe from the set of points and to generate a pose of the lidar sensor based on the keyframe.