无初始条件的视觉惯性导航：高动态环境下构建环境中的定位与技术实现
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本文主要探讨了"视觉惯性辅助导航在高动态运动环境中无初始条件的应用"（Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions）。作者是Todd Lupton和Salah Sukkarieh，发表于2012年2月的IEEE Transactions on Robotics，卷28，第1期。研究的核心是提出了一种新颖的方法，旨在融合来自惯性测量单元（IMU）和视觉传感器的数据，以便快速且线性地恢复惯性集成的初始条件，如重力估计，无需专门的初始化步骤。这种方法采用图形化的同时定位与建图（SLAM）策略，确保了输出的实时性和稳定性。
文章的技术重点在于解决高动态环境下的导航问题，其中关键技术包括：
1. 无初始条件融合：通过算法设计，能够迅速和连续地处理IMU数据和视觉输入，即使在运动初态不确定的情况下也能提供即时的导航信息，避免了传统方法中对初始状态的依赖。
2. 可观性分析：文中深入探讨了系统的可观性问题，即如何保证在复杂的室内环境中，传感器组合能够提供足够的信息来确定位置、速度和姿态等关键参数。
3. 实时尺度估计：论文还讨论了如何实现实时的系统尺度估计，这对于导航的准确性至关重要，特别是在建筑物内复杂的三维空间中。
4. 传感器校准与校正：系统能在线进行IMU的校准和视觉传感器的对齐，保证了数据的精确性。
5. 融合方式：两种视觉传感器技术——立体视觉和单眼视觉都被应用到实时数据融合中，以提高鲁棒性和适应性。
实验结果展示了该系统在实际建筑环境中的性能，包括平台的位置、速度、姿态以及重力向量和传感器校准的实时估计。整体而言，这项工作为解决高动态环境中的导航问题提供了一种创新且实用的解决方案，具有重要的理论和实践价值。
64 IEEE TRANSACTIONS ON ROBOTICS, VOL. 28, NO. 1, FEBRUARY 2012
Fig. 2. Inertial integration in the body frame of the last pose of interest.
frame before integration. IMUs are sampled at very high rates
when compared with other sensors that are used in navigation,
on the order of hundreds of samples per second, even in low-cost
units.
This requires updates to be performed at high rates for a
marginalizing filter, such as the EKF, or a large number of pose
states being required for delayed state methods. Another prob-
lem this causes, specifically for batch initialized inertial meth-
ods, is that it requires a large number of inertial observations
that are to be stored and processed in the batch filter once the
initial conditions become observable.
If these observations could be integratedfirst without knowing
the initial conditions of the vehicle, then a number of inertial
observations could be treated as a single observation in the filter,
reducing the problems that are listed previously.
One possible way to do this is to integrate the inertial observa-
tions between required poses in the body frame of the previous
pose, as presented in [16]. An illustration of this concept is
shown in Fig. 2, where the frame that is used for integration
of the inertial observations moves along with the vehicle from
pose to pose.
Since in many navigation applications, poses are only re-
quired at the rate of the next fastest sensor other than the IMU,
for example, the frame rate of the camera, and this sensor usually
takes samples at a much lower rate, many inertial observations
can be integrated between poses this way.
If the inertial integration equations (1)–(3) are rewritten to
perform the integration in the body frame of the last pose, the
following equations are obtained:
p
n
t2
= p
n
t1
+(t2 − t1)v
n
t1
+
t2
t1
g
n
dt
2
+ C
n
bt1
t2
t1
C
bt1
bt
f
b
t
− bias
obs
f
dt
2
(4)
v
n
t2
= v
n
t1
+
t2
t1
g
n
dt + C
n
bt1
t2
t1
C
bt1
bt
f
b
t
− bias
obs
f
dt
(5)
φ
n
t2
= φ
n
t1
+ E
n
bt1
t2
t1
E
bt1
bt
ω
b
t
− bias
obs
ω
dt. (6)
The initial conditions for the rotation matrix C
bt1
bt
at the start
of the integration period time t1 is C
bt1
bt1
, which is the Identity
matrix.
These equations still provide the vehicle pose estimates in the
globally referenced navigation frame, but the integration of the
inertial observations between poses is performed in the body
frame of the last pose and then transformed into the navigation
frame after integration instead of before.
One thing to note from (4)–(6) is that the integrations are
performed in the vehicle body frame; the vehicle states with
respect to this frame can be perfectly known. As a result, the
inertial observations can actually be integrated with no initial
condition requirements, and even before the states themselves
are estimated.
If the integrals of the inertial observations from (4)–(6) are
extracted, the following equations are obtained:
Δp
+t1
t2
=
t2
t1
C
bt1
bt
f
b
t
− bias
obs
f
dt
2
(7)
Δv
t1
t2
=
t2
t1
C
bt1
bt
f
b
t
− bias
obs
f
dt (8)
Δφ
t1
t2
=
t2
t1
E
bt1
bt
ω
b
t
− bias
obs
ω
dt. (9)
These terms that can be preintegrated without initial condi-
tions represent the change in position, velocity, and attitude of
the vehicle from pose 1 to pose 2 in the (moving) body frame
of pose 1.
These preintegrated sets of observations can then be used
as a single delta state observation in place of all the IMU ob-
servations that occur between these two poses. Therefore these
integrated terms will be referred to as preintegrated inertial delta
observations.
Once calculated these delta components can then be substi-
tuted back into (4)–(6) as in (10)–(12), shown below. In these
equations the integration of the gravity term has also been sim-
plified, which can be done as the gravity vector integrand con-
tains no time-dependent terms (the
1
2
factor in (10) is a byproduct
of the double integration process)
p
n
t2
= p
n
t1
+(t2 − t1)v
n
t1
+
1
2
(t2 − t1)
2
g
n
+ C
n
bt1
Δp
+t1
t2
(10)
v
n
t2
= v
n
t1
+(t2 − t1)g
n
+ C
n
bt1
Δv
t1
t2
(11)
φ
n
t2
= EulerFromDCM
C
n
bt1
ΔC
bt1
bt2
. (12)
The delta attitude component ΔC
bt1
bt2
is multiplied by the
previous attitude rotation matrix and then converted back into
the Euler representation that is used for state estimation. This
is done to avoid the small-angle approximation that is used by
the Euler rotation rate matrix as this may no longer be valid
for the longer integration integrals used. Furthermore, the delta
attitude component refers to an actual change in attitude over
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