3D Dynamic Object Removal for Multi-Timestep Scene Reconstruction
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Solution Overview
Problem
Existing machine-learned models for robotic platforms, such as autonomous vehicles, struggle to accurately detect and remove dynamic objects from three-dimensional sensor data, leading to incomplete scene representations that obscure static/background features and hinder effective simulation and testing.
Innovation Solution
A machine-learned dynamic object removal model processes multi-modal sensor data across multiple timesteps to generate scene representations by removing dynamic objects, utilizing a coarse-to-fine framework that incorporates geometric, temporal, and intermediate multi-modal information to reconstruct environments with fine-grained details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine-learned models are used for object detection, then the system can detect objects in the environment, but the detection accuracy is insufficient and dynamic objects cannot be effectively removed from scene representations
Solution Approach 1:
The model segments the scene representation by separating dynamic objects from static background features through multi-modal sensor data processing. The system divides the detection task into identifying dynamic objects and reconstructing the static environment without them, improving both detection precision and scene representation reliability
Solution Approach 2:
The patent introduces an intermediate processing stage that uses multi-modal sensor data (LIDAR, camera, radar) as mediators to bridge the gap between raw sensor inputs and accurate object detection. This intermediary processing enables more reliable scene representations by cross-validating detections across multiple sensor types
2Manufacturing precision
If dynamic objects are not removed from sensor data, then the processing is simpler and faster, but the static/background features remain obscured and simulation accuracy is reduced
Solution Approach 1:
The system performs preliminary removal of dynamic objects from sensor data before generating scene representations for simulation. By proactively eliminating dynamic objects in advance, the system ensures that static background features are clearly visible and accurately represented, improving simulation accuracy without requiring complex post-processing
Solution Approach 2:
The patent processes sensor data across multiple dimensions including temporal sequences and multi-modal sensor types to remove dynamic objects. This multi-dimensional approach allows the system to distinguish dynamic from static objects more effectively, achieving high simulation accuracy while managing processing complexity through structured multi-dimensional analysis
3Reliability
If detailed scene representations are generated with fine-grained details, then the simulation realism is improved, but the memory usage and processing time increase
Solution Approach 1:
The system segments the scene representation generation into hierarchical levels of detail, processing only essential features at coarse levels and adding fine-grained details only where necessary for simulation realism. This segmented approach maintains high simulation reliability while reducing overall processing time and memory requirements compared to generating full high-detail representations uniformly
Solution Approach 2:
The patent applies local quality by generating fine-grained detailed representations only in specific regions of the scene where dynamic objects were present or where high detail is critical for simulation realism. Other regions use coarser representations, thereby achieving realistic simulations while optimizing memory usage and processing efficiency through localized detail enhancement
Data Source
AI summary
Systems and methods for generating simulation data based on real-world environments are provided. A method includes obtaining multi-modal sensor data indicative of a dynamic object within an environment of a robotic platform. The multi-modal sensor data is associated with a plurality of timesteps including a first timestep and a second timestep. The method includes providing the multi-modal sensor data indicative of the dynamic object within the environment as an input to a machine-learned dynamic object removal model. And, the method includes receiving as an output of the machine-learned dynamic object removal model, in response to receipt of the multi-modal sensor data, a scene representation indicative of at least a portion of the environment including a reconstructed region based at least in part on removal of the dynamic object and multiple levels of granularity. The scene representation is used as a template for generating different simulations within the depicted environment.