Revisiting Fast and Accurate {RGB}-D Odometry for Real-World Use by Embracing Simplicity

Robust and accurate pose estimation of a robot is essential for many tasks. Over the past decades, researchers have explored odometry estimation using various sensors, including {RGB} cameras, {LiDARs}, and {RGB}-D cameras. {RGB}-D cameras, mainly, are attractive sensors as they provide color and depth. Despite the progress made in the {RGB}-D odometry approaches and systems sharing code, their practical use is often non-trivial due to technical debt in several open-source implementations. In some cases, approaches have been tuned to specific benchmarks, complicating the extension of implementations to different {RGB}-D sensors and environments. This paper introduces an easy-to-use and easy-to-understand yet robust {RGB}-D odometry pipeline adaptable across sensor platforms. We focus on simplicity and contribute a non-black-box {RGB}-D odometry approach and proper implementation to the community. Our open-source system utilizes {ORB} features for correspondence estimation and employs a frame-to-map registration strategy to estimate camera poses. Our system achieves performance on par with state-of-the-art methods while running faster than the sensor frame rate on a single-core {CPU}. Importantly, our approach does not require integrating {IMU} data or additional hardware like {GPUs}, making it easily deployable on various platforms.