Multi-Sensor Obstacle Detection for Delivery Robot 3D Mapping
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Solution Overview
Problem
Autonomous robotic systems face challenges in navigating diverse environments due to the complexity and cost of high-sensitivity sensors and processing resources required for terrain and obstacle detection, which can lead to inefficiencies and increased costs in fulfillment processes like last-mile delivery.
Innovation Solution
A sensor and data analysis system that integrates inputs from RGB, LIDAR, and depth sensors with offline map data to generate a probabilistic 3D map, enabling efficient navigation by dynamically updating terrain and obstacle information, and associating confidence values with map data for improved motion planning and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-sensitivity sensors and processing resources are used for terrain and obstacle detection, then detection accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (RGB camera, LIDAR, depth sensors) into an integrated sensor system that shares processing resources and data pipelines. This merging approach allows the system to achieve high detection accuracy through multi-modal sensing while reducing overall system complexity by consolidating processing functions rather than using separate dedicated systems for each sensor type.
Solution Approach 2:
The processing system is designed to handle multiple sensor inputs (RGB images, LIDAR point clouds, depth data) through a unified architecture that performs terrain detection, obstacle detection, and motion planning functions. This multi-functional processing core reduces device complexity by eliminating the need for separate specialized processing units for each detection task.
2Measurement precision
If high-sensitivity sensors and processing resources are used for terrain and obstacle detection, then detection accuracy is improved, but processing resource consumption increases
Solution Approach 1:
By merging sensor inputs and processing functions into a unified system, the patent reduces redundant processing operations. The integrated architecture processes multiple sensor data types through shared computational resources, achieving high detection accuracy while minimizing energy consumption compared to separate dedicated processing systems for each sensor type.
Solution Approach 2:
The system performs preliminary processing of sensor data including generating a probabilistic 3D map of the environment and identifying terrain characteristics before obstacle detection. This preliminary action organizes and pre-processes data structures, reducing the computational burden during real-time obstacle detection and motion planning, thereby lowering overall processing resource consumption.
3Reliability
If probabilistic 3D map generation with confidence values is implemented, then navigation reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent represents environmental uncertainty through confidence values associated with map data points, transforming qualitative uncertainty into quantitative parameters. This parameter change allows the system to maintain high navigation reliability by incorporating uncertainty information into motion planning while managing data processing complexity through efficient probabilistic data structures and algorithms.
4Productivity
If fast obstacle detection is implemented to match existing delivery processes, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The system performs preliminary processing to generate a probabilistic 3D map and identify terrain characteristics before real-time obstacle detection. This pre-computed environmental model enables faster real-time detection by reducing the computational scope during critical navigation phases, achieving both high productivity and maintained measurement precision.
Solution Approach 2:
The integrated sensor and processing system combines multiple data sources and processing functions into a unified real-time detection pipeline. This merging enables fast obstacle detection by efficiently fusing sensor inputs and leveraging pre-computed map data, achieving delivery speeds comparable to existing processes while maintaining detection accuracy through multi-modal sensing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system allows robotic systems to efficiently navigate through varied environments with reduced processing and resource consumption, enhancing the accuracy and reliability of delivery processes while minimizing collisions and disruptions.
Implementation Method 1
LIDAR sensor data
Implementation Method 2
depth sensor data
Data Source
AI summary
The present disclosure generally relates to a system of a delivery device for combining sensor data from various types of sensors to generate a map that enables the delivery device to navigate from a first location to a second location to deliver an item to the second location. The system obtains data from RGB, LIDAR, and depth sensors and combines this sensor data according to various algorithms to detect objects in an environment of the delivery device, generate point cloud and pose information associated with the detected objects, and generates object boundary data for the detected objects. The system further identifies object states for the detected object and generates the map for the environment based on the detected object, the generated object proposal data, the labeled point cloud data, and the object states. The generated map may be provided to other systems to navigate the delivery device.