Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat (2103.14070v1)

Abstract: In dynamic and cramped industrial environments, achieving reliable Visual Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust method for non-synchronized multi-camera VT&R. Our contribution are expected Camera Performance Models (CPM) which evaluate the camera streams from the teach step to determine the most informative one for localization during the repeat step. By actively selecting the most suitable camera for localization, we are able to successfully complete missions when one of the cameras is occluded, faces into feature poor locations or if the environment has changed. Furthermore, we explore the specific challenges of achieving VT&R on a dynamic quadruped robot, ANYmal. The camera does not follow a linear path (due to the walking gait and holonomicity) such that precise path-following cannot be achieved. Our experiments feature forward and backward facing stereo cameras showing VT&R performance in cluttered indoor and outdoor scenarios. We compared the trajectories the robot executed during the repeat steps demonstrating typical tracking precision of less than 10cm on average. With a view towards omni-directional localization, we show how the approach generalizes to four cameras in simulation. Video: https://youtu.be/iAY0lyjAnqY

• The paper introduces expected Camera Performance Models (CPMs) that evaluate non-synchronized multi-camera streams to select the most informative views for localization.
• The paper demonstrates that proactive camera selection enables tracking precision within an average margin of less than 10 cm in dynamic, confined settings.
• The study validates its approach on a dynamic quadruped robot and extends its applicability to a four-camera framework for omni-directional navigation.

Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat

The paper presents an innovative approach to overcoming challenges faced in dynamic, confined industrial environments, particularly focusing on visual teach and repeat (VT) navigation systems for mobile robots equipped with multiple cameras. The authors introduce a robust methodology for using non-synchronized multi-camera systems by developing expected Camera Performance Models (CPM), which are pivotal in assessing camera streams during the teaching phase to identify the most informative ones for localization during the repetition phase.

Methodology

The core contribution of the paper lies in the introduction of CPMs, which evaluate camera streams from the teaching step to determine the most suitable for localization in the repetition phase. This proactive selection enables the robot to successfully navigate even when a camera view is obstructed, directs towards feature-deficient areas, or when environmental changes occur.

The research also explores VT on a dynamic quadruped robot, ANYmal, where precise path-following is inherently difficult due to the robot's dynamic movement patterns and non-linear walking paths. The paper showcases experiments with both forward and rear-facing stereo cameras, evaluating VT performance in complex indoor and outdoor scenarios. Trajectories executed during the repetition phase demonstrated a typical tracking precision within an average margin of less than 10 cm.

Experimental Results

A key numerical result is the remarkable precision, with tracking accuracy averaging less than 10 cm in demanding environments. Moreover, the method's generalized applicability to a four-camera framework is demonstrated through simulation, forecasting its potential for omni-directional localization.

The inclusion of multiple cameras enriches the system's robustness by providing redundancy in visual sensing. However, this advantage must be weighed against increased computational demand and integration complexity due to synchronization and calibration issues.

Implications

Practically, this research has strong implications for enhancing the reliability of autonomous robots in industrial inspection and monitoring scenarios where environmental dynamics and occlusions pose significant navigation challenges. Theoretically, the paper contributes to the discourse on multi-camera systems by proposing a scalable model that uses less synchronized camera inputs for efficient navigation.

The paper sets a foundation for future explorations in improving perception models and calibration techniques in multi-camera systems. Future developments could aim to extend these methods to incorporate more sophisticated machine learning models that will allow for adaptive learning, perhaps accommodating more significant environmental changes without pre-defined models.

Conclusion

This research strides forward in enhancing VT systems via a multi-camera setup, assisted by the development of CPMs. It demonstrates both the feasibility and effectiveness of utilizing multiple cameras without the need for precise synchronization to achieve robust autonomous navigation in challenging environments. The findings provide a valuable framework for applications demanding high-reliability navigation and open up avenues for more extensive use in diverse, dynamic operational contexts. The work invites further investigation into seamlessly integrating these models into broader autonomous systems, potentially leveraging advancements in AI and machine learning for continual improvement in robot perception and navigation capabilities.