Autonomous Underfloor Void Mapping via 3D Laser Scanner
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Solution Overview
Problem
Autonomous exploration and surveying of underfloor voids and crawl spaces pose challenges due to confined spaces, irregular paths, unknown obstacles, rough terrain, poor illumination, and limitations in wireless communication, making it difficult for mobile robots to operate effectively without operator input.
Innovation Solution
A robotic system equipped with a 3D laser scanner, real-time navigation, and a high-level planner that selects the next best scanning position, using ICP-based alignment and graph optimization to create a global 3D model, combining depth and image data from a camera and laser rangefinder to navigate autonomously and map the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a mobile robot is used for autonomous surveying in underfloor voids, then automation is improved, but reliability deteriorates due to confined spaces, rough terrain, and unknown obstacles
Solution Approach 1:
The surveying task is segmented into multiple scanning positions and depth measurements. The robot performs systematic scanning at different locations and heights to comprehensively map the underfloor void, breaking down the complex autonomous navigation problem into manageable discrete measurement points
Solution Approach 2:
The system performs preliminary depth measurements and 3D mapping before final surveying operations. The robot first scans the environment to build a spatial model, then uses this information to plan subsequent measurement positions, ensuring reliable operation in unknown terrain
2Measurement precision
If depth data from forward and rearward directions are combined, then measurement precision is improved, but device complexity increases due to calibration requirements
Solution Approach 1:
The system uses feedback from comparing forward and rearward depth measurements to detect and correct laser rangefinder calibration errors. By measuring the same physical points from opposite directions and analyzing discrepancies, the system automatically identifies and compensates for measurement biases without requiring complex external calibration equipment
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
Enables effective autonomous mapping and measurement of underfloor voids, improving navigation and data fusion in challenging environments, but requires further development for robust real-time localization and alignment in rubble-filled scenarios.
Implementation Method 1
laser rangefinder being arranged to generate depth data relating to the environment
Data Source
AI summary
The present disclosure provides a method of processing depth data and image data from a robotic device having a camera and a depth measurement device mounted to a chassis of the robotic device to generate a data set representative of a three-dimensional map of an environment in which the robotic device is located. The camera is arranged to generate image data relating to the environment. The depth measurement device is arranged to generate depth data relating to the environment. The method comprises generating image data and depth data at a first location of the robotic device in the environment, whereby to generate a first data set comprising a plurality of data points. The method further comprises moving the robotic device to at least a second location in the environment. The method further comprises generating image data and depth data at the second location, whereby to generate a second data set comprising a plurality of data points. The method further comprises associating each data point of the first data set with the spatially nearest point of the second data set, if any, within a predefined distance from the first data point. The method further comprises replacing data points from the first data set with the associated data points from the second data set by reference to the distance of the data point from the location of the robotic device when the data point was generated.