We investigate the application of the factor graph framework for blind joint
channel estimation and symbol detection on time-variant linear inter-symbol
interference channels. In particular, we consider the expectation maximization
(EM) algorithm for maximum likelihood estimation, which typically suffers from
high complexity as it requires the computation of the symbol-wise posterior
distributions in every iteration. We address this issue by efficiently
approximating the posteriors using the belief propagation (BP) algorithm on a
suitable factor graph. By interweaving the iterations of BP and EM, the
detection complexity can be further reduced to a single BP iteration per EM
step. In addition, we propose a data-driven version of our algorithm that
introduces momentum in the BP updates and learns a suitable EM parameter update
schedule, thereby significantly improving the performance-complexity tradeoff
with a few offline training samples. Our numerical experiments demonstrate the
excellent performance of the proposed blind detector and show that it even
outperforms coherent BP detection in high signal-to-noise scenarios.

我们研究了因子图框架在时间变化的线性符号间干扰信道上盲联合信道估计和符号检测的应用。我们提出了一种基于置信传播算法的后验概率的有效逼近方法，并通过交织后验传播和期望最大化算法的迭代，将检测复杂性进一步降低为每个期望最大化步骤的单个后验传播迭代。此外，我们还提出了一种数据驱动的算法版本，通过引入动量来更新后验传播，并学习合适的期望最大化参数更新调度，从而在少量离线训练样本的情况下显著提高性能与复杂性之间的权衡。数值实验证明了所提出的盲检测器的出色性能，甚至在高信噪比情况下优于相干后验传播检测。

基于数据驱动因子图的盲信道估计与联合符号检测

Blind Channel Estimation and Joint Symbol Detection with Data-Driven  Factor Graphs

Accurate and reliable tracking of multiple moving objects in 3D space is an
essential component of urban scene understanding. This is a challenging task
because it requires the assignment of detections in the current frame to the
predicted objects from the previous one. Existing filter-based approaches tend
to struggle if this initial assignment is not correct, which can happen easily.
We propose a novel optimization-based approach that does not rely on explicit
and fixed assignments. Instead, we represent the result of an off-the-shelf 3D
object detector as Gaussian mixture model, which is incorporated in a factor
graph framework. This gives us the flexibility to assign all detections to all
objects simultaneously. As a result, the assignment problem is solved
implicitly and jointly with the 3D spatial multi-object state estimation using
non-linear least squares optimization. Despite its simplicity, the proposed
algorithm achieves robust and reliable tracking results and can be applied for
offline as well as online tracking. We demonstrate its performance on the real
world KITTI tracking dataset and achieve better results than many
state-of-the-art algorithms. Especially the consistency of the estimated tracks
is superior offline as well as online.

本文提出了基于高斯混合模型和因子图框架的非线性最小二乘优化，用于处理三维空间中多个移动物体的跟踪问题，并展示了在 KITTI 跟踪数据集上实验的表现。

基于因子图的点云 3D 多目标跟踪

Factor Graph based 3D Multi-Object Tracking in Point Clouds

The factor graph framework is a convenient modeling technique for robotic
state estimation where states are represented as nodes, and measurements are
modeled as factors. When designing a sensor fusion framework for legged robots,
one often has access to visual, inertial, joint encoder, and contact sensors.
While visual-inertial odometry has been studied extensively in this framework,
the addition of a preintegrated contact factor for legged robots has been only
recently proposed. This allowed for integration of encoder and contact
measurements into existing factor graphs, however, new nodes had to be added to
the graph every time contact was made or broken. In this work, to cope with the
problem of switching contact frames, we propose a hybrid contact preintegration
theory that allows contact information to be integrated through an arbitrary
number of contact switches. The proposed hybrid modeling approach reduces the
number of required variables in the nonlinear optimization problem by only
requiring new states to be added alongside camera or selected keyframes. This
method is evaluated using real experimental data collected from a Cassie-series
robot where the trajectory of the robot produced by a motion capture system is
used as a proxy for ground truth. The evaluation shows that inclusion of the
proposed preintegrated hybrid contact factor alongside visual-inertial
navigation systems improves estimation accuracy as well as robustness to vision
failure, while its generalization makes it more accessible for legged
platforms.

本文提出了一种混合接触预积分理论，允许从任意数量的接触切换中积分接触信息，其与视觉惯性导航系统结合使用，可以提高状态估计的精度和容错性，并减少非线性优化问题所需的变量数。