Smart Prism with Tilt Compensation for CAN bus on Mobile Machinery Using Robotic Total Stations

Sumesh Sharma; Marcel Moll; Timo Oksanen

eess.SYFeb 27, 2026

Smart Prism with Tilt Compensation for CAN bus on Mobile Machinery Using Robotic Total Stations

Authors: Sumesh Sharma, Marcel Moll, Timo Oksanen

Organizations: Technical University of Munich, Germany; Professorship of Agrimechatronics; Munich Institute of Robotics and Machine Intelligence (MIRMI)

Abstract

Accurate reference trajectories are required to validate autonomous agricultural robots and highly automated off-road vehicles under real-world field conditions. In practice, robotic total stations provide millimeter-level prism center coordinates, but the point of interest on the vehicle is typically displaced by a lever arm, ranging from decimeters to multiple meters. Roll and pitch motions, as typically observed in off-road machinery, therefore introduce horizontal point of interest errors far exceeding the measurement accuracy of robotic total stations observations. This paper presents the design, implementation, and validation of a Smart Prism prototype that augments a robotic total station prism with an inertial measurement unit to enable real-time tilt compensation. The prototype integrates an STM32H7 microcontroller and a Murata SCH16T-series IMU and estimates roll and pitch angles using an adaptive complementary filter. The tilt-compensated point of interest coordinates are obtained by transforming a calibrated lever arm from the body frame into the navigation frame and combining it with robotic total station prism positions. To support vehicle-side integration, the system can transmit prism and tilt-compensated point of interest coordinates on the Controller Area Network bus, allowing the point of interest to be treated as a virtual position sensor (e.g., co-located with a rear-axle reference point). Experiments with a fixed ground reference point, using a prism to point of interest lever arm of approximately 1.07m and manual roll/pitch excursions of up to 60 deg, yield three-dimensional root-mean-square errors between 2.9mm and 23.6mm across five test series. The results demonstrate that IMU-based tilt compensation enables reference measurements suitable for validating centimeter-level navigation systems under dynamic field conditions.

Figures & tables

Figure 1: Smart Prism fully assembled with interface connections in operation.

Figure 2: Smart Prism PCB, consisting of 90 individual components.

Figure 3: Conceptual visualization of the body, navigation and RTS system and POI

Figure 4: Roll/pitch trajectory calculation and associated positioning errors for a test ( $ID=1$ ) with $n=154$ measurement points

ID	$X$	$Y$	$Z$	$\overline{\Delta X}$	$\overline{\Delta Y}$	$\overline{\Delta Z}$	$\sigma_{\Delta X}$	$\sigma_{\Delta Y}$	$\sigma_{\Delta Z}$	$\mathrm{\boldsymbol{RMSE}}_{3D}$	$n$
1	2131.2	998.3	-1537.4	-0.046	-0.160	0.006	2.037	1.809	0.906	2.867	154
2	2423.8	778.5	-1512.1	-0.290	0.239	0.077	8.570	4.930	1.052	9.887	80
3	2621.1	745.8	-1520.4	-0.337	-1.072	-1.651	7.967	4.779	9.652	13.472	92
4	2152.2	954.5	-1536.6	-1.797	-0.511	2.030	19.211	9.625	9.727	23.640	111
5	1702.3	807.7	-1521.2	-2.184	-0.422	0.483	10.566	9.644	9.767	17.406	134

Table 1: Results of the tilt compensation test including reference coordinates. All coordinate and error quantities in this table are given in

mm

;

n

denotes the number of samples.

Figure 5: Concept of vehicle integration: The Smart Prism is mounted on the machine roof, co-located with a standard GNSS antenna.

Jun 9, 2026

Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection

Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high-frequency vibrations inherent in off-road environments. This paper proposes a robust navigation algorithm based on a jerk-augmented Extended Kalman Filter (EKF) integrated with a Multiple Tuning Factor (MTF) adaptation method. Unlike standard EKF approaches that assume constant measurement noise, our method dynamically adjusts the measurement covariance matrix in real-time, allowing the system to cope with sudden disturbances and sensor outliers. We evaluate the algorithm using real-world data from a Salin247 autonomous robot. Results demonstrate that jerk-augmentation combined with MTF adaptation significantly reduces 3D position Root Mean Square Error (RMSE) compared to baseline EKF models, providing superior dead-reckoning capabilities.

Batu Candan, Mohammed Atallah, Simone Servadio +1

Batu Candan, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Mohammed Atallah, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Dr. Simone Servadio, Assistant Professor, Iowa State University, Ames, IA 50011, USA +1

Kalman Filtering Embodied Navigation Ensemble Kalman Filtering Agricultural Robotics Robot State Estimation Inertial Navigation+1

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Mar 30, 2026

A Predictive Control Strategy to Offset-Point Tracking for Agricultural Mobile Robots

Robots are increasingly being deployed in agriculture to support sustainable practices and improve productivity. They offer strong potential to enable precise, efficient, and environmentally friendly operations. However, most existing path-following controllers focus solely on the robot's center of motion and neglect the spatial footprint and dynamics of attached implements. In practice, implements such as mechanical weeders or spring-tine cultivators are often large, rigidly mounted, and directly interacting with crops and soil; ignoring their position can degrade tracking performance and increase the risk of crop damage. To address this limitation, we propose a closed-form predictive control strategy extending the approach introduced in [1]. The method is developed specifically for Ackermann-type agricultural vehicles and explicitly models the implement as a rigid offset point, while accounting for lateral slip and lever-arm effects. The approach is benchmarked against state-of-the-art baseline controllers, including a reactive geometric method, a reactive backstepping method, and a model-based predictive scheme. Real-world agricultural experiments with two different implements show that the proposed method reduces the median tracking error by 24% to 56%, and decreases peak errors during curvature transitions by up to 70%. These improvements translate into enhanced operational safety, particularly in scenarios where the implement operates in close proximity to crop rows.

Stephane Ngnepiepaye Wembe, Vincent Rousseau, Johann Laconte +1

Université Clermont Auvergne, INRAE, UR TSCF, 63000, Clermont-Ferrand, France · SABI AGRI, 63360, Saint-Beauzire, France

Robot Trajectory Tracking Agricultural Robotics Model Predictive Control

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Sep 24, 2026

A Field-Deployable GNSS-based Navigation Stack for Outdoor Mobile Robots

Outdoor robots require more than an accurate receiver and a path-tracking law: the navigation system must preserve geometric consistency from geographic waypoints to actuator commands, expose measurement validity and timing, and respond to invalid or stale state information. This work presents a ROS2 navigation stack with interchangeable single-GNSS--IMU and dual-antenna-GNSS localization front ends. Both provide a common local East--North--Up state interface for pure pursuit, virtual-point cross-track PID, finite-horizon nonlinear model predictive control (NMPC), and a segment-dependent hybrid dispatcher. The architecture specifies coordinate conventions, datum initialization, asynchronous state construction, waypoint geometry, controller equations, quality gates, command arbitration, and watchdog behavior. Independent physical field runs collected during 2025 and 2026 grape-vineyard deployments support a balanced evaluation of 800 runs, with 100 runs for each of eight controller--localization combinations on an approximately 199.6-m route. The row-hybrid mode yields the lowest run-averaged post-acquisition mean absolute cross-track error (MAE) in the evaluated dataset: 0.00952m with single GNSS+IMU and 0.00846~m with dual GNSS. These findings characterize deviations of the recorded positions from the reference route under the evaluated conditions. The open-source navigation software and deployment instructions are available in the https://github.com/YiyuanLinXX/PPBv2/tree/main/PPBv2_Navigation.

Yiyuan Lin, Cole Regnier, Yu Jiang

School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA · School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY, USA

Robot Trajectory Tracking Robot Navigation Robot Localization

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Abstract

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