Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry’s 5.77 cm and 0.1032 deg). We present algorithms for key point extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.
These keypoints must then undergo data association, also known as scan matching @cite_3 @cite_9 in robotics. The most common technique is iterative closest point (ICP) @cite_2 , which iteratively matches points using naive methods until the alignment between keypoint sets is sufficiently close @cite_9 @cite_0 . ICP relies on a good estimate of the relative displacement (i.e., motion prior) between scans. Other data association techniques search for motion parameters that optimize some objective function, like maximizing similarity (e.g., overlap of Gaussian distributions for NDT @cite_8 ) or minimizing distance (e.g., cluster edge difference @cite_1 ) between keypoint sets. Two further examples of objective functions characterize map quality @cite_6 and radar scan distortion @cite_7 in terms of motion. Feature-based approaches associate keypoints using descriptors, like BASD @cite_18 and SURF @cite_26 .
@cite_18 @cite_26 @cite_7 @cite_8 @cite_9 @cite_1 @cite_3 @cite_6 @cite_0 @cite_2
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On the way to achieving higher degrees of autonomy for vehicles in complicated, ever changing scenarios, the localization problem poses a very important role. Especially the Simultaneous Localization and Mapping (SLAM) problem has been studied greatly in the past. For an autonomous system in the real world, we present a very cost-efficient, robust and very precise localization approach based on GraphSLAM and graph optimization using radar sensors. We are able to prove on a dynamically changing parking lot layout that both mapping and localization accuracy are very high. To evaluate the performance of the mapping algorithm, a highly accurate ground truth map generated from a total station was used. Localization results are compared to a high precision DGPS INS system. Utilizing these methods, we can show the strong performance of our algorithm. A vessel navigating in a critical environment such as an archipelago requires very accurate movement estimates. Intentional or unintentional jamming makes GPS unreliable as the only source of information and an additional independent supporting navigation system should be used. In this paper, we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set. Rotating radar sensors are perception systems rarely used in mobile robotics. This paper is concerned with the use of a mobile ground-based panoramic radar sensor which is able to deliver both distance and velocity of multiple targets in its surrounding. The consequence of using such a sensor in high speed robotics is the appearance of both geometric and Doppler velocity distortions in the collected data. These effects are, in the majority of studies, ignored or considered as noise and then corrected based on proprioceptive sensors or localization systems. Our purpose is to study and use data distortion and Doppler effect as sources of information in order to estimate the vehicle's displacement. The linear and angular velocities of the mobile robot are estimated by analyzing the distortion of the measurements provided by the panoramic Frequency Modulated Continuous Wave (FMCW) radar, called IMPALA. Without the use of any proprioceptive sensor, these estimates are then used to build the trajectory of the vehicle and the radar map of outdoor environments. In this paper, radar-only localization and mapping results are presented for a ground vehicle moving at high speed. Abstract For automotive applications, an accurate estimation of the ego-motion is required to make advanced driver assistant systems work reliably. The proposed framework for ego-motion estimation involves two components: The first component is the spatial registration of consecutive scans. In this paper, the reference scan is represented by a sparse Gaussian Mixture model. This structural representation is improved by incorporating clustering algorithms. For the spatial matching of consecutive scans, a normal distributions transform-based optimization is used. The second component is a likelihood model for the Doppler velocity. Using a hypothesis for the ego-motion state, the expected radial velocity can be calculated and compared to the actual measured Doppler velocity. The ego-motion estimation framework of this paper is a joint spatial and Doppler-based optimization function which shows reliable performance on real world data and compared to state-of-the-art algorithms. A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers. The results of a theoretical study of a simple radar edge detection correlation approach are presented. Several candidate edge correlation algorithms are described. Results of a theoretical evaluation of one particular candidate correlation algorithm are presented and verification of the edge correlation concept is provided through a presentation of correlation performance obtained using real radar imagery. Simultaneous localization and mapping (SLAM) builds maps of a priori unknown environments. Whilst this key mobile robotic competency continues to receive substantial attention, less attention has been paid to assessing the quality of the resulting maps. This paper proposes a way to quantify the intrinsic quality of point-cloud maps built from a stream of range bearing measurements. It does so by considering both the temporal and spatial distribution of the points within the map. One of the causes of unsatisfactory maps is the execution of unmodelled or poorly sensed vehicle manoeuvres. In this paper we show that by maximizing the quality of the map as a function of a motion parameterization, the vehicle motion can be recovered while correcting the map at the same time. In contrast to typical scan matching techniques, we do not rely on segmentation of the measurement stream into two separate "scans"; Instead we treat the measurement sequence as a continuous signal. We illustrate the efficacy of this approach by processing range data from a 77 GHz millimeter wave radar that completes 2 rotations per second. We show that despite this acquisition speed being commensurate with vehicle rotation rates, we are able to extract the underlying vehicle motion and yield crisp, well aligned point clouds Ego-motion estimation is a key issue in intelligent vehicles and important to moving objects tracking. In this paper, after a simple overview of existed method, we present a tangent based hybrid real-time ego-motion estimation algorithm based on laser radar, which is composed of iterative tangent weighted closest point (ITCP) and Hough transform based tangent angle histogram (HTAH) algorithms to overcome problems with past methods, such as local minimum, aperture-like, and high computation problem, etc. This algorithm has been tested on both synthetic data and real range data in outdoor environment. Experimental results demonstrate its high accuracy, low computation, wide applicability and high robustness to aperture-like problem, occlusion and noises. The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >