Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization

Abstract

The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of ∼97% over 650 m drives on rough terrain.

Dynamical Analysis of a Navigation Algorithm

“…In this manner, it is proposed to implement Stanley's algorithm as shown in the following equations. Knowing that θ is derivable in time, the value that ω should have is proposed in Equation (1). The side wheels of the AGV used, rotate on the same axis, as represented in Figure 2.…”

“…A traditional approach to the actual problem is to use the wheel odometry as Kilic et al [1] studied, combined with an inertial navigation system, where measurements are taken by an encoder. The present work rejects this method due to the slippage that the wheels suffer during the movement, being necessary data to be predicted.…”

“…To implement the lateral control design, it is necessary to contemplate the sign of e because it is not taken into account in either of Equation (1) or Equation (2). It is necessary to differentiate on which side of the path the AGV is located because depending on this; it must be steered with a positive or negative sign.…”

A Comparison of Robust Kalman Filters for Improving Wheel-Inertial Odometry in Planetary Rovers

“…In this section, we describe the Kalman filter algorithm and CoreN av [5,23] which we use as our base algorithm. The Kalman filter is the most well-known Bayesian filtering algorithm.…”

Slip Estimation for Mars Rover Zhurong Based on Data Drive

“…Visual odometry is a reliable source of information, which is widely used in planetary explorations. Its main working principle is that in the process of movement, image data are collected from the surrounding environment through the camera, a continuous image sequence is used as the input signal, and its motion estimation is obtained by calculating its own pose change [12,13]. However, it has some limitations for planetary rovers, such as the sequence image matching calculation cost being high [14], the feature point matching on the surface features being sparse or the shadow area being limited, and it is not suitable for high-speed operation environments (>0.8 m/s), as the image would appear blurry in a high-speed motion environment [15].…”