3D Semantic Obstacle Mapping for Autonomous Robot Guidance
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Solution Overview
Problem
Autonomous robots face challenges in providing fast, accurate, and reliable positional awareness and environmental recognition, as traditional techniques fail to incorporate real-time sensory data for task planning and adaptation to changing conditions.
Innovation Solution
A robot system employing deep learning techniques, using a trained classifier to detect obstacles and pathways through a combination of RGB and depth cameras, generating a 3D point cloud, and creating an occupancy map for navigation and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional techniques are used for environmental recognition, then device complexity is reduced, but measurement precision and reliability of positional awareness deteriorate
Solution Approach 1:
The system segments environmental recognition into multiple specialized modules: depth camera for distance measurement, RGB camera for visual recognition, LiDAR for 3D mapping, and sonar for obstacle detection. Each sensor type handles specific aspects of environmental perception, improving overall measurement precision while distributing system complexity across modular components.
Solution Approach 2:
The patent introduces deep learning algorithms as intermediaries that process and integrate data from multiple sensors. These AI models act as mediators between raw sensor data and actionable environmental understanding, enabling high-precision positional awareness without requiring direct complex integration of all sensor inputs at the hardware level.
2Adaptability or versatility
If real-time sensory data processing is implemented, then adaptability to changing conditions improves, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing of sensory data through continuous background updating of environmental maps and object databases. By pre-processing and organizing sensor data into structured formats during idle periods, the system reduces real-time processing requirements when rapid adaptation is needed, thus minimizing processing time delays.
Solution Approach 2:
The deep learning models are trained offline on extensive datasets to automatically learn patterns and features of changing environmental conditions. This self-service approach allows the system to adapt to new conditions without requiring extensive real-time computational resources, as the adaptation capability is embedded in the pre-trained models.
3Reliability
If multiple sensor types are integrated, then environmental recognition accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent implements a unified deep learning framework that processes data from multiple sensor types (depth cameras, RGB cameras, LiDAR, sonar) through a common architectural structure. This universal processing approach allows the system to handle diverse sensor inputs with a single integrated algorithmic core, reducing the complexity of sensor integration while maintaining high reliability through multi-sensor data fusion.
4Loss of information
If deep learning models are deployed for object identification, then semantic awareness improves, but computational energy consumption increases
Solution Approach 1:
The system extracts and processes only the most relevant semantic features from sensor data using selective attention mechanisms in the deep learning models. By taking out and focusing computational resources on critical objects and features rather than processing all data equally, the system maintains high semantic awareness while reducing overall energy consumption of the computational system.
Data Source
AI summary
The technology disclosed includes systems and methods for using a deep learning trained classifier to detect obstacles and pathways in an environment in which a robot moves. The system includes logic to receive image information captured by at least one visual spectrum-capable camera and location information captured by at least one depth measuring camera located on a mobile platform. The system includes logic to determine a three-dimensional 3D point cloud of points having 3D information. The system can determine, using an ensemble of trained neural network classifiers, an identity for objects. The system includes logic to determine an occupancy map of the environment. The system includes logic to provide the occupancy map to a process for initiating robot movement to avoid objects in the occupancy map of the environment.