Depth-Aided Visual Inertial Odometry Pose Estimation
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Solution Overview
Problem
Current visual inertial odometry (VIO) systems face challenges in accurately estimating object pose using data from cameras and inertial measurement units (IMUs), particularly in environments with complex motion and varying depth measurements, leading to inaccuracies in tracking six degrees of freedom and 3D reconstruction.
Innovation Solution
The proposed method incorporates a visual inertial odometry system that processes measurements from IMUs, cameras, and depth sensors to determine keyframe residues and generate an optimized sliding window graph, enabling accurate estimation of object pose by integrating depth measurements into the factor graph optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth measurements are integrated into VIO systems, then object pose estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines depth sensor data with traditional vision and IMU measurements into a unified factor graph optimization framework. The depth-aided VIO system merges multiple data sources (2D image features, 3D depth measurements, and inertial data) to jointly estimate camera pose and 3D scene structure, thereby improving measurement precision while managing system complexity through integrated processing.
Solution Approach 2:
The factor graph optimization framework serves multiple functions simultaneously: it processes 2D image features, incorporates 3D depth measurements, integrates IMU data, and estimates both camera trajectory and 3D scene structure. This multi-functional approach allows the system to handle diverse sensor inputs and achieve accurate pose estimation without requiring separate processing pipelines for each sensor type.
2Measurement precision
If depth-aided VIO is used in aggressive motion scenarios, then tracking precision is improved, but computational load increases
Solution Approach 1:
The system dynamically adjusts its processing based on motion characteristics. During aggressive motion, the factor graph optimization leverages the complementary strength of depth measurements and IMU data to maintain tracking precision when visual features become unreliable. The optimization framework adaptively weights different measurement types based on their reliability under current motion conditions, improving tracking precision while managing computational resources efficiently.
Solution Approach 2:
The patent modifies the optimization parameters and measurement models to account for aggressive motion scenarios. The factor graph incorporates depth measurements with appropriate uncertainty modeling that adapts to motion intensity, allowing the system to maintain accurate pose estimation during rapid movements. The system changes its processing parameters dynamically based on the reliability of different sensor inputs under varying motion conditions.
3Measurement precision
If sliding window graph optimization is performed with depth residue, then initialization accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing of depth measurements and pre-computes depth residues before the main factor graph optimization. By preparing depth-related data structures and computing depth residuals in advance, the system reduces the computational burden during the actual optimization phase, thereby improving initialization accuracy while minimizing the additional processing time required.
Data Source
AI summary
A method and an apparatus are provided for performing visual inertial odometry (VIO). Measurements are processed from an inertial measurement unit (IMU), a camera, and a depth sensor. Keyframe residue including at least depth residue is determined based on the processed measurements. A sliding window graph is generated and optimized based on factors derived from the keyframe residue. An object pose is estimated based on the optimized sliding window graph.