Bidirectional 3D Detection Backbone for Temporal Robot Perception
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Solution Overview
Problem
Existing machine-learning-based solutions for scene understanding in autonomous robots lack accuracy and temporal consistency in object detection and require separate training of detectors and trackers, leading to suboptimal performance.
Innovation Solution
A 3D detection backbone that performs feature-level temporal aggregation bidirectionally, incorporating past, present, and future information, and integrates object detection and tracking capabilities, using a similarity objective to improve detector performance and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine-learning-based solutions are used for scene understanding, then object detection can be performed, but detection accuracy and temporal consistency deteriorate
Solution Approach 1:
The patent combines object detection and tracking into a unified 3D detection backbone that performs feature-level temporal aggregation bidirectionally. This merging of detection and tracking functions into a single integrated system resolves the contradiction by enabling both high detection accuracy and temporal consistency through shared feature representations that capture both spatial and temporal information.
Solution Approach 2:
The patent performs offline bidirectional temporal aggregation that incorporates future information into current feature representations before online inference. This preliminary action of pre-computing temporally-aggregated features allows the system to achieve high temporal consistency during online operation without the computational burden of bidirectional processing in real-time.
2Ease of manufacture
If separate training of detectors and trackers is performed, then each component can be optimized independently, but overall system performance deteriorates
Solution Approach 1:
The patent merges detection and tracking into a unified 3D detection backbone with shared feature extractors that perform bidirectional temporal aggregation. This integration allows joint optimization of detection and tracking objectives, enabling the system to achieve superior overall performance compared to separate training approaches while maintaining the flexibility to optimize different components independently through modular loss functions.
3Reliability
If feature-level temporal aggregation with bidirectional recurrence is performed, then temporal consistency improves, but computational complexity increases
Solution Approach 1:
The patent performs the computationally intensive bidirectional temporal aggregation offline before online inference. By pre-computing temporally-aggregated features from training data or offline processing, the system achieves high temporal consistency during online operation with reduced computational burden, as the heavy lifting of bidirectional recurrence is completed in advance.
Solution Approach 2:
The patent segments the processing into offline feature aggregation and online inference stages. The computationally complex bidirectional temporal aggregation is performed offline to generate preprocessed feature representations, while online operation uses these precomputed features for faster inference, thus reducing real-time computational complexity while maintaining temporal consistency.
Data Source
AI summary
Systems and methods described herein relate to generating perception data. In one embodiment, a system extracts, in an offline processing environment, first features from a time sequence of perceptual sensor data to generate a first set of bird's-eye-view (BEV) feature images. The system also extracts second features from the first set of BEV feature images using a BEV feature extractor that performs bidirectional feature-level temporal aggregation to generate a second set of BEV feature images. The system also consumes the second set of BEV feature images using one or more neural-network heads to perform one of: (1) generating automatically labeled perception data to train one or more of an online perception model, an online prediction model, and an online planning model used to control an autonomous robot and (2) validating the performance of an online autonomous stack used to control an autonomous robot.