Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization (1909.03423v3)

Abstract: In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors. For ground robots, the wheel odometer is widely used in pose estimation tasks, especially in applications under planar-scene based environments. However, since the wheel odometer only provides 2D motion estimates, it is extremely challenging to use that for performing accurate full 6D pose (3D position and 3D orientation) estimation. Traditional methods on 6D pose estimation either approximate sensor or motion models, at the cost of accuracy reduction, or rely on other sensors, e.g., inertial measurement unit (IMU), to provide complementary measurements. By contrast, in this paper, we propose a novel method to utilize the wheel odometer for 6D pose estimation, by modeling and utilizing motion manifold for ground robots. Our approach is probabilistically formulated and only requires the wheel odometer and an exteroceptive sensor (e.g., a camera). Specifically, our method i) formulates the motion manifold of ground robots by parametric representation, ii) performs manifold based 6D integration with the wheel odometer measurements only, and iii) re-parameterizes manifold equations periodically for error reduction. To demonstrate the effectiveness and applicability of the proposed algorithmic modules, we integrate that into a sliding-window pose estimator by using measurements from the wheel odometer and a monocular camera. By conducting extensive simulated and real-world experiments, we show that the proposed algorithm outperforms competing state-of-the-art algorithms by a significant margin in pose estimation accuracy, especially when deployed in complex large-scale real-world environments.

• The paper presents a novel quadratic polynomial manifold representation that enhances 6D pose accuracy beyond traditional planar assumptions.
• The integration process fuses wheel odometry and exteroceptive measurements without relying on extra sensors like IMUs.
• Re-parameterization dynamically updates the motion manifold, significantly improving estimation performance in complex real-world environments.

Analyzing Pose Estimation Techniques for Non-Holonomic Ground Robots: A Probabilistic Approach to Manifold Representation

The academic paper titled "Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization" explores the advanced methodologies of motion estimation for non-holonomic ground robots. This research introduces a probabilistic framework for fusing measurements from wheel odometers and exteroceptive sensors, focusing particularly on 6D pose estimation. Traditional methods have been limited by planar surface assumptions or the need for additional sensors like IMUs to complement the wheel odometer data. This paper, however, presents a novel method to attain high-accuracy 6D pose estimates by leveraging motion manifold modeling.

Core Contributions

1. Motion Manifold Representation: The authors propose a quadratic polynomial-based representation to capture the motion manifold of ground robots. This approach contrasts with traditional zeroth or first-order representations, which limit motion to planar surfaces, unsuitable for complex outdoor environments where this research aims to extend applicability.
2. 6D Integration Process: The paper introduces a new integration method, employing 6D pose integration through a motion manifold that ensures consistency in orientation derived from odometer measurements. This methodological shift removes the necessity for additional sensors like the IMU while maintaining accuracy.
3. Manifold Re-Parameterization: The research identifies the challenge of dynamically changing manifold parameters during spatial movement and addresses it through a periodic re-parameterization process. This approach enhances the estimation performance by continuously updating the manifold representation according to the trajectory of the robot.

Validation Through Experiments

The proposed method underwent rigorous testing in both simulated environments and real-world experiments. The results from simulations demonstrated that the manifold-based methods significantly outperformed traditional planar-based odometer approaches, especially in extensive 3D spaces with non-planar characteristics. Real-world tests further confirmed the robustness of the approach, particularly in urban driving scenarios where the manifold-based models provided superior estimation accuracy compared to the state-of-the-art methods that integrate IMU data.

Implications and Future Scope

The implications of this research are substantial in the scope of autonomous ground robotics. The ability to reduce the dependency on high-cost sensors such as IMUs while achieving comparable or superior estimation accuracies fosters broader applicability and scalability of autonomous ground navigation solutions.

In terms of future developments, there is potential exploration of the integration of this manifold-based method with machine learning techniques for dynamic adaptation of the manifold parameters in real time. Additionally, further research could explore integrating these methods with varied sensor configurations or adapting them to environments with more complex spatial characteristics.

In conclusion, this paper makes significant strides in pose estimation for ground robotics, advocating for robust probabilistic methods that enhance the feasibility and accuracy of navigation systems. The proposed techniques stood out by demonstrating their capability to maintain high precision in challenging dynamic environments, thus contributing valuable insights to the field of autonomous robotics.