Actor Direction Detection Using LIDAR and Camera Fusion
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Solution Overview
Problem
Determining the direction of an actor within a physical space is challenging due to sparse and non-uniform point cloud data from LIDAR sensors, which lacks rich and detailed information, and is prone to blind spots, making it difficult to infer additional information such as orientation and movement.
Innovation Solution
A system that combines point cloud data from LIDAR sensors with image data from cameras to determine the direction of an actor's face and body, using image data mapping to infer the actor's direction, and adjusting robotic device operations based on this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used to capture spatial data, then depth information and 3D structure are obtained, but the data is sparse and non-uniform, making it difficult to determine actor direction and orientation
Solution Approach 1:
The patent combines LIDAR point cloud data with camera image data to create a fused representation of the actor. The LIDAR provides depth and 3D structure while the camera provides rich visual details and facial orientation information. By merging these two data sources, the system overcomes the sparsity issue of LIDAR alone and achieves accurate actor direction determination.
Solution Approach 2:
The patent uses image data as an intermediary to bridge the gap between LIDAR point cloud data and actor orientation determination. The camera captures images that show the actor's face and body orientation, providing visual information that complements the spatial data from LIDAR and enables accurate direction inference despite LIDAR's sparsity.
2Reliability
If only point cloud data is used, then 3D spatial structure is captured, but blind spots occur and detailed information about actor orientation is lost
Solution Approach 1:
The system merges LIDAR point cloud data with camera image data to compensate for blind spots and information loss. While LIDAR provides 3D spatial structure, the camera fills in the gaps by capturing visual details including facial orientation, body posture, and surrounding context that LIDAR alone cannot detect.
Solution Approach 2:
The camera serves multiple functions: it captures visual appearance, determines facial orientation, identifies body posture, and provides context about the actor's surroundings. This multi-functionality allows the system to overcome the limitations of LIDAR alone in determining actor direction and orientation.
3Measurement precision
If image data mapping is performed to determine actor direction, then rich detailed information is obtained, but computational complexity increases
Solution Approach 1:
The patent segments the actor detection process into distinct modules: LIDAR point cloud processing, camera image processing, and fusion/direction determination. By segmenting the complex task into manageable parts, the system can process and fuse data from multiple sources systematically, reducing overall computational complexity while maintaining high precision.
Solution Approach 2:
The system performs preliminary processing on both LIDAR and camera data separately before fusion. Pre-processing steps include filtering point clouds, detecting faces in images, and preparing data structures for efficient fusion. This preliminary action reduces the computational burden during the actual direction determination phase.
Data Source
AI summary
Example systems and methods are disclosed for determining the direction of an actor based on image data and sensors in an environment. The method may include receiving point cloud data for an actor at a location within the environment. The method may also include receiving image data of the location. The received image data corresponds to the point cloud data received from the same location. The method may also include identifying a part of the received image data that is representative of the face of the actor. The method may further include determining a direction of the face of the actor based on the identified part of the received image data. The method may further include determining a direction of the actor based on the direction of the face of the actor. The method may also include providing information indicating the determined direction of the actor.