Mobile Robot 3D Object Modeling for Accurate Dynamic Agent Poses
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Solution Overview
Problem
Existing autonomous vehicle perception systems inaccurately represent the shape and pose of dynamic agents in sensor data, leading to errors in scenario visualization and safety-affecting decisions.
Innovation Solution
A method optimizing a cost function to model 3D objects in sensor data, incorporating shape and motion parameters, and rendering accurate visualizations in a GUI, using prior knowledge of object classes and sensor modalities to refine perception data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional perception systems use simple bounding boxes and basic pose estimation, then the system complexity is low, but the accuracy of representing 3D object shape and pose deteriorates
Solution Approach 1:
The patent transforms the representation parameters from simple bounding box coordinates to comprehensive 6D pose parameters (position x, y, z and orientation qx, qy, qz, qw) plus shape parameters. This parameter transformation enables accurate 3D object representation by capturing both geometric shape and spatial orientation information that simple bounding boxes cannot represent.
Solution Approach 2:
The patent transitions from 2D image plane bounding boxes to 3D spatial representation with 6D pose estimation. By adding depth dimension and orientation parameters, the system achieves accurate 3D localization and shape representation, moving from planar 2D detection to volumetric 3D understanding.
2Loss of information
If perception systems process and visualize all sensor data in real-time, then the completeness of information is high, but the computational time and processing speed deteriorate
Solution Approach 1:
The patent extracts only the essential 6D pose and shape parameters from comprehensive sensor data, separating critical information (position, orientation, shape) from redundant data. This extraction approach maintains information completeness for safety-critical parameters while reducing computational burden by focusing processing on key features.
Solution Approach 2:
The system performs preliminary 6D pose estimation and shape parameter extraction before detailed visualization and analysis. By pre-processing and structuring the data in advance, the system reduces subsequent processing time while ensuring all necessary information is captured and organized for efficient rendering.
3Reliability
If the system uses detailed 3D shape modeling and occlusion analysis, then the accuracy of detecting missed objects improves, but the computational complexity and processing load deteriorate
Solution Approach 1:
The patent introduces 3D shape models as intermediary representations between raw sensor data and occlusion analysis. These parametric shape models serve as mediators that encode object geometry in a compact form, enabling efficient occlusion detection by comparing projected shapes with detected bounding boxes without requiring full detailed 3D reconstruction.
Solution Approach 2:
The system creates simplified 3D shape copies or proxies of actual objects using parametric models. These copied representations capture essential geometric features needed for occlusion analysis while being computationally inexpensive to manipulate, allowing rapid detection of occluded regions without processing full-resolution 3D data.
Data Source
AI summary
The present disclosure relates to techniques for locating and modelling a 3D object captured by a mobile robot. A cost function is defined over a set of variables, and is applied to sensor data. The set of variables comprises shape parameters of a 3D object model and a time sequence of poses of the 3D object model. The cost function penalizes inconsistency between the sensor data and the set of variables. The object belongs to a known object class, and the 3D object model or the cost function encodes expected 3D shape information associated with the known object class. The 3D object is modelled by tuning poses of the object and the shape parameters, to optimize the cost function. A visualization of a location of the robot and an object shape representing the 3D object is rendered in a graphical user interface (GUI)