Robot Multi-Sensor Localization Using Synchronized Odometry
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Solution Overview
Problem
Existing robot localization methods using multiple sensors face accuracy issues due to time gaps between information processing by different sensors, leading to reduced localization precision.
Innovation Solution
A method where a robot synchronizes multiple sensors, such as 2D LiDAR and camera sensors, by generating odometry information from wheel encoder data and using a controller to coordinate the acquisition and processing of data from each sensor, ensuring synchronized information use for enhanced localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to enhance localization accuracy, then measurement precision is improved, but loss of time increases due to different processing times of each sensor
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing odometry information from wheel encoders before actual localization is needed. The system prepares candidate positions in advance based on wheel encoder data, so when sensor data arrives (even with time delays), the localization can be performed immediately using pre-computed reference positions, thereby compensating for the time gaps between different sensor processing speeds
Solution Approach 2:
The patent introduces wheel encoder odometry information as an intermediary element that bridges the time gap between different sensors. The wheel encoder provides continuous position estimates that serve as a reference framework, allowing the system to align and synchronize LiDAR and camera data that arrive at different times, thus mediating the temporal mismatch between sensors with different processing speeds
2Measurement precision
If multiple sensors with different processing speeds are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple sensor data streams (LiDAR, camera, wheel encoder) into a unified localization framework by combining their respective odometry information. The wheel encoder provides a base odometry that is integrated with LiDAR odometry and visual odometry, creating a combined estimation that simplifies the handling of multiple sensors by treating them as complementary sources rather than separate complex systems to be synchronized
Solution Approach 2:
The wheel encoder serves multiple functions simultaneously: it provides direct position estimation, generates candidate positions for comparison with sensor data, and acts as a reference framework for synchronizing other sensors. This multi-functionality reduces overall system complexity by having a single component perform multiple synchronization and localization tasks
Data Source
AI summary
Disclosed herein are a method of localization by synchronizing multi sensors and a robot implementing the same. The robot according to an embodiment includes a controller that, when a first sensor acquires first type information, generates first type odometry information using the first type information, that, at a time point when the first type odometry information is generated, acquires second type information by controlling a second sensor and then generates second type odometry information using the second type information, and that the robot by combining the first type odometry information and the second type odometry information.