Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry

Gargiulo, Anna Maria; Stefano, Ivan di; Genova, Antonio

doi:10.3390/app11125490

Cited by 6 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…VO represents a key localization technique that enables accurate updates of the pose of moving assets in the operational environment, although localization error will eventually accumulate over long traverses. To support safe navigation operations on demanding terrains (Gargiulo et al, 2021a), data‐fusion approaches can be adopted to process additional measurements, such as sun sensors and star‐trackers observations, and LiDAR data (Carle & Barfoot, 2010; Carle et al, 2010; Gargiulo et al, 2021b). Local bundle‐adjustment techniques can be also employed to reduce the drift of the reconstructed trajectory by jointly refining the position of the rover and the landmarks locations, enabling an accurate extended motion estimation.…”

Section: Discussionmentioning

confidence: 99%

Precise pose estimation of the NASA Mars 2020 Perseverance rover through a stereo‐vision‐based approach

Andolfo

Petricca

Genova

2022

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

Visual Odometry (VO) is a fundamental technique to enhance the navigation capabilities of planetary exploration rovers. By processing the images acquired during the motion, VO methods provide estimates of the relative position and attitude between navigation steps with the detection and tracking of twodimensional (2D) image keypoints. This method allows one to mitigate trajectory inconsistencies associated with slippage conditions resulting from dead-reckoning techniques. We present here an independent analysis of the high-resolution stereo images of the NASA Mars 2020 Perseverance rover to retrieve its accurate localization on sols 65, 66, 72, and 120. The stereo pairs are processed by using a 3D-to-3D stereo-VO approach that is based on consolidated techniques and accounts for the main nonlinear optical effects characterizing real cameras. The algorithm is first validated through the analysis of rectified stereo images acquired by the NASA Mars Exploration Rover Opportunity, and then applied to the determination of Perseverance's path. The results suggest that our reconstructed path is consistent with the telemetered trajectory, which was directly retrieved onboard the rover's system. The estimated pose is in full agreement with the archived rover's position and attitude after short navigation steps. Significant differences (~10-30 cm) between our reconstructed and telemetered trajectories are observed when Perseverance traveled distances larger than 1 m between the acquisition of stereo pairs.

show abstract

Section: Discussionmentioning

confidence: 99%

Precise pose estimation of the NASA Mars 2020 Perseverance rover through a stereo‐vision‐based approach

Andolfo

Petricca

Genova

2022

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since outside of the Earth's surface global positioning is not currently available, rovers must rely on relative position estimation, where positioning is typically achieved by means of dead reckoning techniques [52], possibly enhanced by sensor fusion, where information coming from different sensors is fused together, typically by leveraging the Extended Kalman Filter (EKF) [53] or Bayesian approaches [54]. An example of this can be found in planetary rovers that use positioning systems that fuse readings from of the following: wheel encoders (wheels odometry-WO), Inertial Measurement Units (IMU) [55], sun sensors [51], and visual odometry (VO) [56]. Even though odometry is especially vulnerable to errors that increase over time (drift), most notably in high-slip scenarios, an accurate pose estimation is essential for closing the navigation control loop of the vehicle [57].…”

Section: Introductionmentioning

confidence: 99%

The Archimede Rover: A Comparison Between Simulations and Experiments

Caruso,

Giberna,

Görner

et al. 2023

Robotics

View full text Add to dashboard Cite

In this paper, we propose an in-depth evaluation of the performance of the Archimede rover while traversing rough terrain with loose soil. In order to better analyze this, the reality gap is evaluated when simulating the behavior with an open-source simulator. To this extent, we implement a full model of the rover in the open-source dynamics simulator Gazebo, along with several types of terrains that replicate the experimental conditions. The rover control system is equipped with a kinematics model that allows for driving in different modes. We implement an odometric system aboard the rover, as well as an external optical absolute tracking system as reference. We estimate the drift occurring during driving in different configurations, two types of soil with corresponding wheel geometries. The results show good adherence of the odometry when the rover drives on planar ground; conversely, as expected, a marked influence of slope is seen on wheel drift. The reality gap between simulations and experimental results is kept comparatively small provided that slopes are not present.

show abstract

“…An alternative to direct odometry calibration from the information gathered from the wheels is the application of data fusion from different sensors. Gargiulo et al [4] estimated the mobile robot position and orientation by fusing information gathered from the wheels and an Inertial Measurement Unit (IMU). Zwierzchowski et al [5] used a similar approach and included the information gathered from a vision system that measures the distance between the robot and custom markers located in the surrounding space.…”

Section: Introductionmentioning

confidence: 99%

Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories

2022

View full text Add to dashboard Cite

Odometry is a simple and practical method that provides a periodic real-time estimation of the relative displacement of a mobile robot based on the measurement of the angular rotational speed of its wheels. The main disadvantage of odometry is its unbounded accumulation of errors, a factor that reduces the accuracy of the estimation of the absolute position and orientation of a mobile robot. This paper proposes a general procedure to evaluate and correct the systematic odometry errors of a human-sized three-wheeled omnidirectional mobile robot designed as a versatile personal assistant tool. The correction procedure is based on the definition of 36 individual calibration trajectories which together depict a flower-shaped figure, on the measurement of the odometry and ground truth trajectory of each calibration trajectory, and on the application of several strategies to iteratively adjust the effective value of the kinematic parameters of the mobile robot in order to match the estimated final position from these two trajectories. The results have shown an average improvement of 82.14% in the estimation of the final position and orientation of the mobile robot. Therefore, these results can be used for odometry calibration during the manufacturing of human-sized three-wheeled omnidirectional mobile robots.

show abstract

Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry

Cited by 6 publications

References 30 publications

Precise pose estimation of the NASA Mars 2020 Perseverance rover through a stereo‐vision‐based approach

Precise pose estimation of the NASA Mars 2020 Perseverance rover through a stereo‐vision‐based approach

The Archimede Rover: A Comparison Between Simulations and Experiments

Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories

Contact Info

Product

Resources

About