Ceiling-Feature Vision Navigation for Indoor Drift Correction
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Solution Overview
Problem
Existing autonomous vehicle navigation systems that rely on ceiling features for indoor navigation face challenges such as high dependency on ceiling lights, manual landmark setup, significant orientation errors, and cumulative drift issues, especially in environments with repetitive or sparse features.
Innovation Solution
A system and method using a ceiling-facing camera to detect and utilize naturally occurring ceiling lines for navigation, eliminating the need for pre-designated infrastructure, and employing a vision feedback control loop to maintain orientation and minimize drift errors by selecting the best line based on heuristics and adapting to changing lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ceiling lights or manually set landmarks are used for navigation, then the autonomous vehicle can navigate indoor environments, but the system becomes highly dependent on pre-installed infrastructure and manual setup
Solution Approach 1:
The system uses naturally occurring ceiling features (lights, tiles, beams, ducts) that already exist in the environment, eliminating the need for manual landmark installation. The vehicle autonomously detects and utilizes these pre-existing features for navigation, making the system self-sufficient and adaptable to any indoor environment without modification.
Solution Approach 2:
The navigation system can work with multiple types of ceiling features (lights, tiles, beams, ducts, strapping) simultaneously, making it universally applicable to various building types including warehouses, greenhouses, and commercial buildings without requiring environment-specific customization.
2Measurement precision
If ceiling lines are used for orientation correction, then the robot can maintain its path, but accumulated error reaches up to 10%
Solution Approach 1:
The system continuously monitors the position of ceiling lines in the camera field of view and uses this feedback to calculate drift in real-time. By constantly comparing the expected position of ceiling features with their actual detected position, the system generates corrective steering commands to eliminate drift accumulation and maintain accurate trajectory over long distances.
Solution Approach 2:
The patent replaces traditional mechanical odometry and orientation sensors with a vision-based system that uses ceiling line detection. This substitution eliminates mechanical error accumulation by using optical feedback from the environment, achieving higher long-term trajectory accuracy without relying on mechanical wheel encoders or inertial measurement units.
3Measurement precision
If a highly accurate depth camera is used to measure ceiling line distance, then the trajectory misalignment can be measured accurately, but the system becomes costly to implement
Solution Approach 1:
The system replaces expensive depth cameras with a standard 2D monocular camera. By using perspective geometry and the known vertical orientation of ceiling features, the system calculates trajectory misalignment from 2D image coordinates without requiring costly 3D depth sensing, significantly reducing hardware costs while maintaining measurement accuracy.
Solution Approach 2:
The patent changes the measurement parameter from 3D depth (requiring expensive depth cameras) to 2D image coordinates. By using the vertical vanishing point and ceiling line intersections in 2D image space, the system derives trajectory accuracy information without needing direct depth measurement, transforming a high-cost problem into a low-cost solution.
4Measurement precision
If ceiling features are used for localization, then the vehicle position can be determined, but localization is lost in environments with repetitive or insufficient ceiling features
Solution Approach 1:
The system uses the vertical dimension of ceiling features (their position above the vehicle) to create a unique localization signature. Even when ceiling features are repetitive horizontally, their vertical positions and relationships provide additional dimensional information that distinguishes different locations, enabling reliable localization in environments with repetitive patterns.
Data Source
AI summary
Autonomous vehicles utilize sensors to determine their position on the ground. These sensors suffer from cumulative errors which cause the vehicle's position to be compromised. The present invention eliminates such error in the orthogonal axis from the direction of travel. The present invention provides a system and method for navigating an autonomous vehicle, using various overhead features. A vision subsystem comprises at least one camera pointed towards the ceiling. The camera is preferably pointed at a pitch angle of 90 degrees with respect to the vehicle, and is pointed overhead the autonomous vehicle towards the ceiling. The vision subsystem scans the ceiling features of the building the autonomous vehicle is in, and is able to self-determine which ceiling features it will utilize for navigation while minimizing drift errors, allowing the vehicle to maintain a straight path without requiring the installation of any additional infrastructure on the ceiling.