3D Fork Slot Position Estimation for Tilted Forklift Cameras
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Solution Overview
Problem
Existing forklift systems face inaccuracies in inserting forks into pallet slots due to camera tilt, leading to improper positioning.
Innovation Solution
An information processing apparatus that includes a detection unit to identify fork insertion slots and forks, an acquisition unit to capture depth information, a correction unit to refine depth data, and a position estimation unit to accurately estimate three-dimensional positions using edge detection and depth images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a camera is mounted on the forklift to detect pallet position, then the forklift can operate autonomously, but the position detection accuracy deteriorates when the camera is tilted
Solution Approach 1:
The system changes the parameter representation from 2D image coordinates to 3D spatial coordinates by incorporating depth information. The conversion unit transforms the detected 2D pallet position into 3D position data, accounting for camera tilt and depth variations, thereby maintaining measurement precision regardless of camera mounting orientation
Solution Approach 2:
The invention transitions from two-dimensional image plane detection to three-dimensional spatial position detection. By adding the depth dimension through depth image processing and coordinate transformation, the system accurately determines pallet position in 3D space even when the camera is tilted, resolving the contradiction between autonomous operation capability and position detection accuracy
2Device complexity
If only two-dimensional images are used for position detection, then the system complexity is reduced, but the three-dimensional position estimation accuracy deteriorates
Solution Approach 1:
The system merges 2D image data from the camera with 3D depth data from the depth sensor to create accurate three-dimensional position estimates. The conversion unit integrates information from both 2D images and depth images, combining the simplicity of 2D detection with the accuracy of 3D measurement without requiring complex standalone 3D detection hardware
3Loss of time
If depth information is not corrected, then the processing time is reduced, but the shape recognition accuracy in depth images deteriorates
Solution Approach 1:
The system performs preliminary correction of depth information using the detected 2D shape data before final 3D position estimation. The conversion unit pre-adjusts the depth values based on the known 2D shape characteristics, so that when depth images are displayed or processed further, the shapes are already recognized with high accuracy, reducing the need for additional complex processing steps
Data Source
AI summary
An information processing apparatus (10) includes: a detection unit (11) configured to detect, using an image and forks for supporting the transport pallet are captured, a shape of fork insertion slots into which the forks are inserted and a shape of the forks; an acquisition unit (12) configured to acquire a depth image showing depth information of the fork insertion slots and depth information of the forks in the image; a correction unit (13) configured to correct, using the shape of the fork insertion slots and the shape of the forks that are detected, the depth information so that the shape of the fork insertion slots and the shape of the forks can be recognized in the depth image; and a position estimation unit (14) configured to estimate three-dimensional positions of the fork insertion slots and three-dimensional positions of the forks based on the depth information.