Self-Supervised Depth and Ego-Motion With GPS Scale Consistency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Monocular self-supervised depth and ego-motion estimation methods suffer from scale ambiguity and introduce scale-inconsistency in estimated depth and ego-motion across different video snippets due to the reliance on appearance-based losses without long sequence constraints.
Innovation Solution
The introduction of a 'GPS to scale loss' (G2S) is used to enforce scale-consistency and awareness by synchronizing GPS coordinates with images and calculating a loss function that exponentially increases in weight during training, combining appearance-based photometric loss, smoothness loss, and GPS to scale loss, which accounts for noise and systemic bias in GPS measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If appearance-based losses are used for training monocular depth and ego-motion networks, then training can be performed on small video sub-sequences without ground truth, but scale ambiguity and scale-inconsistency across different video snippets occur
Solution Approach 1:
The patent introduces GPS coordinates as an intermediary signal to bridge the scale ambiguity problem. GPS provides external metric-scale references that mediate between the appearance-based loss (which is scale-invariant) and the desired metric-scale consistent depth and ego-motion estimates. The GPS-to-scale loss function uses GPS-measured translation as a mediator to enforce scale consistency across different video snippets.
Solution Approach 2:
The patent changes the training parameter by introducing a new loss term (GPS-to-scale loss) that operates on a different parameter space (GPS coordinates and translation magnitude) than the traditional appearance-based loss. This parameter change allows the network to learn scale-consistent representations by optimizing against GPS-derived translation constraints while maintaining the benefits of self-supervised appearance-based training.
2Measurement precision
If GPS coordinates are synchronized and used to calculate GPS to scale loss, then scale-consistency is enforced, but device complexity and computational overhead increase
Solution Approach 1:
The patent extracts only the essential component from GPS data (translation magnitude) needed for scale enforcement, rather than processing all GPS coordinates and temporal alignments. By taking out only the translation magnitude component and using it in a simplified loss function, the system achieves scale consistency without the full complexity of comprehensive GPS processing.
3Measurement precision
If GPS to scale loss weight exponentially increases during training, then scale awareness is improved, but training stability may be compromised
Solution Approach 1:
The patent makes the GPS-to-scale loss weight dynamic rather than static, allowing it to exponentially increase during training. This dynamic adjustment enables the system to gradually prioritize scale consistency as training progresses, improving scale awareness while managing training stability through controlled temporal evolution of the loss weight.
Solution Approach 2:
The exponential increase in GPS-to-scale loss weight creates a periodic training regime where different loss components are emphasized at different training stages. Early training focuses more on appearance-based losses while later training emphasizes GPS-to-scale constraints, creating a structured periodic pattern in loss weighting that balances stability and scale awareness.
Data Source
AI summary
A computer-implemented method to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein complementary GPS coordinates synchronized with the images are used to calculate a GPS to scale loss to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. A relative weight assigned to the GPS to scale loss exponentially increases as training progresses. The depth and ego-motion prediction neural networks are trained using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions.