6D pose estimation is a common and important task in industry. Obtaining the 6D pose of objects is the basis for many other functions such as bin picking, autopilot, etc. Therefore, many corresponding studies have been made in order to improve the accuracy and enlarge the range of application of various approaches. After several years of development, the methods of 6D pose estimation have been enriched and improved. Although some predecessors have analyzed the methods and summarized them in detailed, there have been many new breakthroughs in recent years. To understand 6D pose estimation better, this paper will make a new and more detailed review of 6D pose estimation. We divided these methods into two approaches: Learning-based approaches and non-learning-based approaches, including 2D-information-based approach and 3D-information-based approach. Additionally, we introduce the challenges that exist in 6D pose estimation. Finally, we compare the performance of different methods qualitatively and discuss the future development trends of the 6D pose estimation.

6D Pose Estimation of Objects: Recent Technologies and Challenges

This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT) for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT’s performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated) pulse radar, SAR (Synthetic aperture radar), or ISAR (Inverse synthetic aperture radar), for improving the probability of target recognition.

https://www.mdpi.com/1424-8220/16/10/1559/pdf

Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT.

Multidetection (MD) systems are characterized by multiple observation modes (OMs), and hence, simultaneously produce multiple measurements for each target. The key challenge in exploiting MD systems for multitarget tracking (MTT), compared to single-detection (SD) systems, is the significant amount of extra computational burden involved in order to solve the resulting multidimensional assignment problem among measurements, targets, and OMs. This article presents a novel computationally efficient MTT framework for MD systems, wherein the multitarget state is modeled as a random finite set (RFS), and a bank of OM-dependent MTT RFS filters with SD model are employed to recursively provide OM-dependent posteriors. The latter, which contain both real and false targets, are then suitably fused so as to enhance consensus on the true targets while weakening trust on the existence of the false ones. In this way, the computational complexity is significantly reduced compared to existing MTT algorithms with the MD model. Two representative RFS filters, i.e., unlabeled probability hypothesis density (PHD) and labeled multi-Bernoulli (LMB), are considered in the proposed framework and the computational complexity of the resulting MD MTT algorithms is analyzed. Performance of the proposed approach is assessed by simulation experiments in both over-the-horizon-radar (OTHR) and single-frequency-network passive radar (SFN-PR) MTT applications.

Fusion-Based Multidetection Multitarget Tracking With Random Finite Sets

A parametric method for non-stationary interference suppression in direct sequence spread-spectrum systems

Multiple-target tracking has increasingly gained attention over the last 60 years, with the data association task being one of the most challenging aspects in sensor data fusion due to its computational burden. Hence, a plethora of algorithms has been proposed to solve this data association problem. However, most approaches are solely evaluated in comparison to algorithms of the same class. Therefore, this paper tries to give an overview and intends to evaluate systematically the four main classes of multiple-target tracking filters, namely the non-Bayesian data association filters, the Bayesian data association filters, the intensity filters and the multi-Bernoulli filters. These four classes are exemplified by respective filters, namely the Global Nearest Neighbor filter, the Joint Probabilistic Data Association filter, the Probability Hypothesis Density filter and the Poisson multi-Bernoulli mixture filter. These four filters are evaluated on two challenging simulated scenarios comprising situations with false measurements as well as birth and death of targets. The performance is assessed using the well-established GOSPA metric. It is shown that the Poisson multi-Bernoulli mixture filter outperforms the other three filters regarding the GOSPA metric in these scenarios, yielding a smaller mean error and deviation in its position estimates. This accuracy comes at the cost of a higher runtime performance, as the other three filter types require less computational time to produce their state estimates.

Systematic Analysis of the PMBM, PHD, JPDA and GNN Multi-Target Tracking Filters

https://cyberleninka.org/article/n/1431.pdf

DOA estimation for attitude determination on communication satellites

An efficient approximate implementation for labeled random finite set filtering

Array response interpolation and DOA estimation with array response decomposition

Vehicle pose estimation plays an significant role in vehicle tracking which is an important part in autonomous driving. In this paper, a new vehicle pose estimation method based on Edge Distance is proposed. The Edge Distance is defined to describe the distribution of point clouds with respect to their bounding rectangles. Based on the Edge Distance, a bounding rectangle of the point clouds is utilized to fit the vehicle outline. For the case where the point clouds are sparse or only one edge is visible, the bounding rectangle is modified according to preset vehicle size. The pose estimation of the vehicle is obtained based on the bounding rectangle. Experiments based on the KITTI data sets show that the proposed method can obtain better performance in vehicle pose estimation compared with the method based on modified scaling series.

Vehicle Pose Estimation Based on Edge Distance Using Lidar Point Clouds (Poster)

High-performance array applications often require an accurate array response model. A common way to achieve this is by array calibration which involves measuring the response for a finite number of given source directions and employing interpolation. This paper considers the array calibration problem by combing interpolation techniques and parametric modeling. The idea is to model the array response as a product of a mutual coupling matrix, an ideal array response vector (derived from the geometry of antenna array) and an angle-dependent correction vector. Since the major effects are captured by the physical model and the mutual coupling matrix, the correction vector will be a smoother function of angle as compared to direct interpolation of the measured array response. In numerical experiments of a real antenna array, the method is found to improve the performance of the array calibration significantly.

Bin Yang

Papers

DOA estimation for attitude determination on communication satellites

An efficient approximate implementation for labeled random finite set filtering

Array response interpolation and DOA estimation with array response decomposition

Vehicle Pose Estimation Based on Edge Distance Using Lidar Point Clouds (Poster)

Array calibration using array response interpolation and parametric modeling