Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing tool, especially in vision-obstructed environments, as it is less affected by particles than lidars or cameras. Modern 4D imaging radars provide three-dimensional geometric information and relative velocity measurements, but they bring challenges, such as a small field of view and sparse, noisy point clouds. Detecting loop closures in SLAM is critical for reducing trajectory drift and maintaining map accuracy. However, the directional nature of 4D radar data makes identifying loop closures, especially from reverse viewpoints, difficult due to limited scan overlap. This article explores using 4D radar for loop closure in SLAM, focusing on similar and opposing viewpoints. We generate submaps for a denser environment representation and use introspective measures to reject false detections in feature-degenerate environments. Our experiments show accurate loop closure detection in geometrically diverse settings for both similar and opposing viewpoints, improving trajectory estimation with up to 82% improvement in ATE and rejecting false positives in self-similar environments.

We present a novel 3D odometry method that recovers the full motion of a vehicle only from a Doppler-capable range sensor. It leverages the radial velocities measured from the scene, estimating the sensor’s velocity from a single scan. The vehicle’s 3D motion, defined by its linear and angular velocities, is calculated taking into consideration its kinematic model which provides a constraint between the velocity measured at the sensor frame and the vehicle frame.Experiments carried out prove the viability of our single-sensor method compared to mounting an additional IMU. Our method provides a more reliable translation of the sensor, compared to the errors linked to IMUs due to noise and biases. Its short-term accuracy and fast operation (∼5ms) make it a proper candidate to supply the initialization to more complex localization algorithms or mapping pipelines. Not only does it reduce the error of the mapper, but it does so at a comparable level of accuracy as an IMU would. All without the need to mount and calibrate an extra sensor on the vehicle.

There is a current increase in the development of "4D" Doppler-capable radar and lidar range sensors that produce 3D point clouds where all points also have information about the radial velocity relative to the sensor. 4D radars in particular are interesting for object perception and navigation in low-visibility conditions (dust, smoke) where lidars and cameras typically fail. With the advent of high-resolution Doppler-capable radars comes the possibility of estimating odometry from single point clouds, foregoing the need for scan registration which is error-prone in feature-sparse field environments. We compare several odometry estimation methods, from direct integration of Doppler/IMU data and Kalman filter sensor fusion to 3D scan-to-scan and scan-to-map registration, on three datasets with data from two recent 4D radars and two IMUs. Surprisingly, our results show that the odometry from Doppler and IMU data alone give similar or better results than 3D point cloud registration. In our experiments, the position drift can be as low as 0.9% over 1.8 and 4.5km trajectories. That allows accurate estimation of 6-DOF ego-motion over long distances also in feature-sparse mine environments. These results are useful not least for applications of navigation with resource-constrained robot platforms in feature-sparse and low-visibility conditions such as mining, construction, and search & rescue operations.

While autonomous navigation of mobile robots on rigid terrain is a well-explored problem, navigating on deformable terrain such as tall grass or bushes remains a challenge. To address it, we introduce an explainable, physics-aware and end-to-end differentiable model which predicts the outcome of robot-terrain interaction from camera images, both on rigid and non-rigid terrain. The proposed MonoForce model consists of a black-box module which predicts robot-terrain interaction forces from onboard cameras, followed by a white-box module, which transforms these forces and a control signals into predicted trajectories, using only the laws of classical mechanics. The differentiable white-box module allows backpropagating the predicted trajectory errors into the black-box module, serving as a self-supervised loss that measures consistency between the predicted forces and ground-truth trajectories of the robot. Experimental evaluation on a public dataset and our data has shown that while the prediction capabilities are comparable to state-of-the-art algorithms on rigid terrain, MonoForce shows superior accuracy on nonrigid terrain such as tall grass or bushes. To facilitate the reproducibility of our results, we release both the code and datasets.

In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long distances, total stations provide accurate and precise measurements, that are unaffected by the usual factors that deteriorate the accuracy of Global Navigation Satellite System (GNSS). While a single robotic total station can track the position of a target in three Degrees Of Freedom (DOF), three robotic total stations and three targets are necessary to yield the full six DOF pose reference. Since it is crucial to express the position of targets in a common coordinate frame, we present a novel extrinsic calibration method of multiple robotic total stations with field deployment in mind. The proposed method does not require the manual collection of ground control points during the system setup, nor does it require tedious synchronous measurement on each robotic total station. Based on extensive experimental work, we compare our approach to the classical extrinsic calibration methods used in geomatics for surveying and demonstrate that our approach brings substantial time savings during the deployment. Tested on more than 30 km of trajectories, our new method increases the precision of the extrinsic calibration by 25 % compared to the best state-of-the-art method, which is the one taking manually static ground control points.

Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other lower-rate modalities. Moreover, in the absence of constant acceleration, the gravity vector makes two attitude angles out of three observable in the global coordinate frame. In visual odometry, this is already being used to reduce the 6-Degrees Of Freedom (DOF) pose estimation problem to 4-DOF. In lidar SLAM, the gravity measurements are often used as a penalty in the back-end global map optimization to prevent map deformations. In this work, we propose an Iterative Closest Point (ICP)-based front-end which exploits the observable DOF and provides pose estimates aligned with the gravity vector. We believe that this front-end has the potential to support the loop closure identification, thus speeding up convergences of global map optimizations. The presented approach has been extensively tested against accurate ground-truth localization in large-scale outdoor environments as well as in the Subterranean Challenge organized by Defense Advanced Research Projects Agency (DARPA). We show that it can reduce the localization drift by 30% when compared to the standard 6-DOF ICP. Moreover, the code is readily available to the community as a part of the libpointmatcher library.

Localization of mobile robots can be challenging in highly dynamic environments (airport terminals, stores, hospitals) yet these areas offer great potential for robotics applications. HighWiFi coverage provides a way to localize even low-cost robots that do not need to be equipped with expensive exteroceptive sensors or excessive computational power to run visual-based SLAM algorithms. Since current trend in robotics is in mass-deployment of low-cost, replaceable robots, we focus our efforts in this direction as well. Possible solution is combination of high-tech robots deployed as support assuring reliable localization for the low-cost robots that are actually doing the job. The proposed scenario is to use one SLAM robot to map WiFi signal strength in the working area and provide it to a low-cost robot to correct drift of its 6-DOF gyro-odometry localization system. For this purpose, we extend Gaussian-processes-based WiFi localization algorithms to full 3D and experimentally evaluate the proposed approach in 2 mapping and 7 localization sorties performed on different dates spanning four months.

This paper proposes a novel approach to improving precision and reliability of odometry of skid-steer mobile robots by means inspired by robotic terrain classification (RTC). In contrary to standard RTC approaches we do not provide human labeled discrete terrain categories but we classify the terrain directly by the values of coefficients correcting the robot's odometry. Hence these coefficients make the odometry model adaptable to the terrain type due to inherent slip compensation. Estimation of these correction coefficients is based on feature extraction from the vibration data measured by an inertial measurement unit and regression function trained offline. Statistical features from the time domain, frequency domain, and wavelet features were explored and the best were automatically selected. To provide ground truth trajectory for the purpose of offline training a portable overhead camera tracking system was developed. Experimental evaluation on rough outdoor terrain proved 67.9±7.5% improvement in RMSE in position with respect to a state of the art odometry model. Moreover, our proposed approach is straightforward, easy for online implementation, and low on computational demands.

Precise and reliable estimation of orientation plays crucial role for any mobile robot operating in unknown environment. The most common solution to determination of the three orientation angles: pitch, roll, and yaw, relies on the Attitude and Heading Reference System (AHRS) that exploits inertial data fusion (accelerations and angular rates) with magnetic measurements. However, in real world applications strong vibration and disturbances in magnetic field usually cause this approach to provide poor results. Therefore, we have devised a new approach to orientation estimation using inertial sensors only. It is based on modified complementary filtering and was proved by precise laboratory testing using rotational tilt platform as well as by robot field-testing. In the final, the algorithm well outperformed the commercial AHRS solution based on magnetometer aiding.