Ego-pose estimation and dynamic object tracking are two key issues in an
autonomous driving system. Two assumptions are often made for them, i.e. the
static world assumption of simultaneous localization and mapping (SLAM) and the
exact ego-pose assumption of object tracking, respectively. However, these
assumptions are difficult to hold in highly dynamic road scenarios where SLAM
and object tracking become correlated and mutually beneficial. In this paper,
DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This
method integrates the state estimations of both the ego vehicle and the static
and dynamic objects in the environment into a unified optimization framework,
to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we
implement object detection to remove all the points that belong to potential
dynamic objects. Then, LiDAR odometry is conducted using the filtered point
cloud. At the same time, detected objects are associated with the history
object trajectories based on the time-series information in a sliding window.
The states of the static and dynamic objects and ego vehicle in the sliding
window are integrated into a unified local optimization framework. We perform
SLAM and object tracking simultaneously in this framework, which significantly
improves the robustness and accuracy of SLAM in highly dynamic road scenarios
and the accuracy of objects' states estimation. Experiments on public datasets
have shown that our method achieves better accuracy than A-LOAM.

本文提出 DL-SLOT，一种动态激光雷达 (Lidar) SLAM 和动态目标跟踪方法，该方法将车辆和静态 / 动态物体的状态估计融合到统一的优化框架中，同时进行 SLAM 和目标跟踪，以改善高度动态路况下 SLAM 的鲁棒性和准确性以及物体状态估计的准确性。

基于图优化的动态激光雷达 SLAM 和物体跟踪

DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

In this paper we present DOT (Dynamic Object Tracking), a front-end that
added to existing SLAM systems can significantly improve their robustness and
accuracy in highly dynamic environments. DOT combines instance segmentation and
multi-view geometry to generate masks for dynamic objects in order to allow
SLAM systems based on rigid scene models to avoid such image areas in their
optimizations.
To determine which objects are actually moving, DOT segments first instances
of potentially dynamic objects and then, with the estimated camera motion,
tracks such objects by minimizing the photometric reprojection error. This
short-term tracking improves the accuracy of the segmentation with respect to
other approaches. In the end, only actually dynamic masks are generated. We
have evaluated DOT with ORB-SLAM 2 in three public datasets. Our results show
that our approach improves significantly the accuracy and robustness of
ORB-SLAM 2, especially in highly dynamic scenes.

本文介绍了一种动态物体跟踪的方法，称为 DOT (Dynamic Object Tracking)，该方法结合了实例分割和多视角几何来生成动态物体的掩模，可以显著提高现有 SLAM 系统在高度动态环境下的鲁棒性和准确性。我们通过在三个公共数据集上对 ORB-SLAM 2 进行评估，结果表明我们的方法显著提高了 ORB-SLAM 2 的准确性和鲁棒性，特别是在高度动态的场景下。

DOT: 动态物体跟踪的视觉 SLAM

DOT: Dynamic Object Tracking for Visual SLAM

We demonstrate the use of shape-from-shading (SfS) to improve both the
quality and the robustness of 3D reconstruction of dynamic objects captured by
a single camera. Unlike previous approaches that made use of SfS as a
post-processing step, we offer a principled integrated approach that solves
dynamic object tracking and reconstruction and SfS as a single unified cost
function. Moving beyond Lambertian S f S , we propose a general approach that
models both specularities and shading while simultaneously tracking and
reconstructing general dynamic objects. Solving these problems jointly prevents
the kinds of tracking failures which can not be recovered from by pipeline
approaches. We show state-of-the-art results both qualitatively and
quantitatively.

本文提出一种基于 shape-from-shading（SfS）的单一摄像头拍摄的动态物体的 3D 重建的优化方法，采用一种基于统一代价函数的综合方法来同时解决动态目标跟踪和重建和 SfS。