This paper details the design and implementation of a system for predicting
and interpolating object location coordinates. Our solution is based on
processing inertial measurements and global positioning system data through a
Long Short-Term Memory (LSTM) neural network and polynomial regression. LSTM is
a type of recurrent neural network (RNN) particularly suited for processing
data sequences and avoiding the long-term dependency problem. We employed data
from real-world vehicles and the global positioning system (GPS) sensors. A
critical pre-processing step was developed to address varying sensor
frequencies and inconsistent GPS time steps and dropouts. The LSTM-based
system's performance was compared with the Kalman Filter. The system was tuned
to work in real-time with low latency and high precision. We tested our system
on roads under various driving conditions, including acceleration, turns,
deceleration, and straight paths. We tested our proposed solution's accuracy
and inference time and showed that it could perform in real-time. Our
LSTM-based system yielded an average error of 0.11 meters with an inference
time of 2 ms. This represents a 76\% reduction in error compared to the
traditional Kalman filter method, which has an average error of 0.46 meters
with a similar inference time to the LSTM-based system.

该研究报告详细介绍了基于惯性测量和全球定位系统数据的长短期记忆 (LSTM) 神经网络和多项式回归的对象位置坐标的预测与插值系统的设计和实现。通过对真实世界车辆和全球定位系统传感器的数据处理，我们的 LSTM 系统在实时低延迟和高精度中展现了较传统的卡尔曼滤波方法更低的平均误差和类似的推断时间。

基于 LSTM 神经网络和多项式回归的实时物体定位预测

Object Location Prediction in Real-time using LSTM Neural Network and  Polynomial Regression

We propose using global orientation from inertial measurements, and the bias
it induces on the shape of objects populating the scene, to inform visual 3D
reconstruction. We test the effect of using the resulting prior in depth
prediction from a single image, where the normal vectors to surfaces of objects
of certain classes tend to align with gravity or be orthogonal to it. Adding
such a prior to baseline methods for monocular depth prediction yields
improvements beyond the state-of-the-art and illustrates the power of gravity
as a supervisory signal.

利用来自惯性测量的全局方向和对场景内对象形状的偏差，来指导视觉三维重建，通过将得出的先验应用于单张图片深度预测的实验，发现某些类别的物体表面法向量可能趋向于与重力对齐或者垂直于重力，添加此先验在基准方法的基础上可以获得超过现有技术的改进，这展示了重力作为一种监督信号的强大效用。

地理监督的视觉深度预测

Geo-Supervised Visual Depth Prediction

We present an unsupervised deep neural network approach to the fusion of
RGB-D imagery with inertial measurements for absolute trajectory estimation.
Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns
to perform visual-inertial odometry (VIO) without inertial measurement unit
(IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias
or white noise) or the extrinsic calibration between an IMU and camera. The
network learns to integrate IMU measurements and generate hypothesis
trajectories which are then corrected online according to the Jacobians of
scaled image projection errors with respect to a spatial grid of pixel
coordinates. We evaluate our network against state-of-the-art (SOA)
visual-inertial odometry, visual odometry, and visual simultaneous localization
and mapping (VSLAM) approaches on the KITTI Odometry dataset and demonstrate
competitive odometry performance.

本研究介绍了一种无监督深度神经网络方法，用于融合 RGB-D 图像和惯性测量进行绝对轨迹估计。我们的网络在没有 IMU 固有参数或 IMU 与相机之间的外部校准的情况下进行学习，学习整合 IMU 测量并生成假设轨迹，然后根据空间像素坐标的缩放图像投影误差的雅可比矩阵进行在线更正。在 KITTI Odometry 数据集上与最先进的视觉惯性测距，视觉测距和视觉同时定位和地图构建（VSLAM）方法进行了比较，表现出有竞争力的测距性能。