AI Object Placement Using LIDAR Room Models
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Solution Overview
Problem
Existing technologies lack efficient systems for accurately visualizing proposed changes to homes, representing properties, viewing object placement, providing AI-based recommendations, and generating precise 3D models for landscape design and utility line visualization.
Innovation Solution
Utilizing LIDAR technology and AI to measure dimensions, build 3D models, and display modified representations, while employing machine learning for object placement recommendations and generating accurate 3D floor plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR technology is used to measure dimensions and build 3D models, then measurement precision and manufacturing precision are improved, but device complexity increases
Solution Approach 1:
The patent uses LIDAR technology as an intermediary device to capture spatial data and generate 3D models of rooms and objects. The LIDAR system serves as a mediator between physical space measurement and digital representation, enabling precise dimension capture without requiring complex manual measurement procedures. The generated 3D models then serve as intermediaries for visualizing furniture placement and receiving AI recommendations.
Solution Approach 2:
The patent replaces traditional mechanical measurement methods (tape measures, manual dimension taking) with optical LIDAR-based measurement systems. This substitution eliminates the need for physical contact measurement tools and manual data recording, automatically generating precise 3D spatial representations through laser ranging and time-of-flight measurements.
2Ease of operation
If AI-based recommendations are implemented for object placement, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system implements self-service through AI algorithms that automatically analyze the 3D room model, object dimensions, and spatial constraints to generate placement recommendations without requiring user expertise in interior design or spatial planning. The AI autonomously processes the data and provides optimized furniture arrangement suggestions, making the system accessible to ordinary users.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system presents AI-generated placement recommendations to the user, who can then accept, reject, or request alternative suggestions. The system learns from user preferences and interactions to refine future recommendations, creating an adaptive feedback loop that improves ease of operation over time.
3Loss of information
If detailed 3D models and AI recommendations are provided, then loss of information is reduced, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing LIDAR data to generate complete 3D models of the space and pre-calculating spatial relationships, object fitment possibilities, and optimal placement configurations before user interaction. This preliminary processing reduces the time required during actual use, as the heavy computational work is already completed and results are ready for immediate presentation.
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 precise visualization of changes and object placement, provides accurate inventory mapping, and minimizes time spent in stores by offering detailed 3D navigation and inventory management.
Implementation Method 1
LIDAR is a technology that measures distance to a target by illuminating the target (e.g., using laser light) and then measuring the reflected light with a sensor (e.g., measuring the time of flight from the laser signal source to its return to the sensor)
Implementation Method 2
LIDAR is a technology that measures distance to a target by illuminating the target (e.g., using laser light) and then measuring the reflected light with a sensor
Data Source
AI summary
The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: trains a machine learning algorithm based upon preexisting data of object placement in a room; receives room data comprising dimensional data of a room; receives object data comprising: (i) dimensional data of an object; (ii) a type of the object; and/or (iii) color data of the object; and with the trained machine learning algorithm, generates a recommendation for placement of the object in the room based upon: (i) the received room data, and (ii) the received object data.