Multi-modal Sensor Registration for Vehicle
Perception via Deep Neural Networks

arXiv:1412.7006v2 [] 8 Jul 2015

Michael Giering, Vivek Venugopalan and Kishore Reddy
United Technologies Research Center
E. Hartford, CT 06018, USA
Email: {gierinmj, venugov, reddykk}@utrc.utc.com
Abstract—The ability to simultaneously leverage multiple
modes of sensor information is critical for perception of an automated vehicle’s physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a
prerequisite to analyzing the fused data. The persistence and
reliability of multi-modal registration is therefore the key to the
stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars
are a common example of where keeping the LiDAR and video
channels registered to common physical features is important.
We develop a deep learning method that takes multiple channels
of heterogeneous data, to detect the misalignment of the LiDARvideo inputs. A number of variations were tested on the Ford
LiDAR-video driving test data set and will be discussed. To the
best of our knowledge the use of multi-modal deep convolutional
neural networks for dynamic real-time LiDAR-video registration
has not been presented.

I. M OTIVATION
Navigation and situational awareness of optionally manned
vehicles requires the integration of multiple sensing modalities
such as Light Detection and Ranging (LiDAR) and video,
but could just as easily be extended to other modalities
including Radio Detection And Ranging (RADAR), ShortWavelength Infrared (SWIR) and Global Positioning System
(GPS). Spatio-temporal registration of information from multimodal sensors is technically challenging in its own right. For
many tasks such as pedestrian and object detection tasks that
make use of multiple sensors, decision support methods rest
on the assumption of proper registration. Most approaches [1]
in LiDAR-video for instance, build separate vision and LiDAR
feature extraction methods and identify common anchor points
in both. Alternatively, by generating a single feature set on
LiDAR, Video and optical flow, it enables the system to to
capture mutual information among modalities more efficiently.
The ability to dynamically register information from the available data channels for perception related tasks can alleviate
the need for anchor points between sensor modalities. We see
auto-registration as a prerequisite need for operating on multimodal information with confidence.
Deep neural networks (DNN) lend themselves in a seamless manner for data fusion on time series data. For some
challenges in which the modalities share significant mutual
information, the features generated on the fused information
can provide insight that neither input alone can [2]. In effect
the ML version of, ”the whole is greater than the sum of it’s
parts”.

Autonomous navigation places significant constraints on the
speed of perception algorithms and their ability to drive decision making in real-time. Though computationally intensive to
train, our implemented DCNN run easily within our real-time
frame rates of 8 fps and could accommodate more standard
rates of 30 fps. With most research in deep neural networks
focused on algorithmic improvements and novel applications,
a significant benefit to applied researchers is sometimes under
appreciated. The automated feature generation of DNNs enables us to create mutli-modal systems with far less overhead.
The need for domain experts and hand-crafted feature design
are lessened, allowing more rapid prototyping and testing.
The generalization of auto-registration across multiple assets
is clearly a path to be explored.
In this paper, the main contributions are: (i) formulation
of an image registration problem as a fusion of modalities
from different sensors, namely LIDAR (L), video (Grayscale
or R,G,B) and optical flow (U,V); (ii) performance evaluation
of deep convolutional neural networks (DCNN) with various
input parameters, such as kernel filter size and different combinations of input channels (R,G,B,Gr,L,U,V); (iii) fusion of
patch-level and image-level predictions to generate alignment
at the frame-level. The experiments were conducted using a
publicly available dataset from FORD and the University of
Michigan [3]. The DCNN implementation was executed on an
NVIDIA Tesla K40 GPU with 2880 cores and compute power
of 5 TFLOPS (single precision). The paper is organized into
the following sections: Section I describes the introduction and
motivation for this work; Section II provides a survey of the
related work; the problem formulation along with the dataset
description and the preprocessing is explained in Section III;
Section IV gives the details of the DCNN setup for the
different experiments; Section V describes the experiments and
the post-processing steps for visualizing the qualitative results;
finally Section VI summarizes the paper and concludes with
future research thrusts.
II. P REVIOUS W ORK
A great amount has been published on various multi-modal
fusion methods [4], [5], [6], [7]. The most common approaches
taken generate features of interest in each modality separately
and create a decision support mechanism that aggregates
features across modalities. If spatial alignment is required
across modalities, as it is for LiDAR-video such filter methods

[8] are required to ensure proper inter-modal registration.
These filter methods for leveraging 3D LiDAR and 2D images
are often geometric in nature and make use of projections
between the different data spaces.
Automatic registration of 2D video and 3D LiDAR has been
a widely researched topic for over a decade [9], [10], [11],
[1]. Its application in real-time autonomous navigation makes
it a challenging problem. Majority of the 2D-3D registration
algorithms are based on feature matching. Geometric features
like corners and edges are extracted from detected vanishing
points [12], [13], line segments [14], [15], and shadows [16].
Feature based approaches generally rely on dense 3D point
cloud and additional knowledge of relative sun position and
GPS/inertial navigation system (INS). Another approach used
for video and LiDAR auto-registration is to reconstruct 3D
point cloud from video sequences using structure from motion
(SFM) and performing 3D-3D registration [17], [18]. 3D-3D
registration is more difficult and computationally expensive
compared to 2D-3D registration.
The use of deep neural networks to analyze multi-modal
sensor inputs has increased sharply in just the last few years,
including audio-video [2], [19], image/text [20], image/depth
[21] and LiDAR-video To the best of our knowledge the use of
multi-modal deep neural networks for dynamic LiDAR-video
registration has not been presented.
A common challenge for data fusion methods is deciding at
what level features from the differing sensor streams should be
brought together. The deep neural network (DNN) approach
most similar to the more traditional data fusion methods is
to train DNNs independently on sensor modalities and then
use the high-level outputs of those networks as inputs to a
subsequent aggregator, which could also be a DNN. This is
analogous to the earlier example of learning 3D/2D features
and the process of identifying common geometric features.
It is possible however to apply DNNs with a more agnostic
view enabling a unified set of features to be learned across
multi-modal data. In these cases the input channels aren’t
differentiated. Unsupervised methods including deep Boltzmann machines and deep auto-encoders for learning such joint
representations have been successful.
Deep convolutional neural networks (DCNN) enable a
similar agnostic approach to input channels. A significant
difference is that target data is required to train them as
classifiers. This is the approach chosen by us for automating
the registration of LiDAR-video and optical-flow, in which we
are combining 1D/3D/2D data representations respectively to
learn a unified model across as many as 6D.
III. P ROBLEM S TATEMENT
Being able to detect and correct the misalignment (registration, calibration) among sensors of the same or different
kinds, is critical for decision support systems operating on
their fused information streams. For our work DCNNs were
implemented for the detection of small spatial misalignments
in LiDAR and Video frames. The methodology is directly
applicable to temporal registration as well. LiDAR-video data

collected from a driverless car was chosen for the multi-modal
fusion test case. LiDAR-video is a common combination for
providing perception capabilities to many types of ground and
airborne platforms including driverless cars [8].
A. Ford LiDAR-video Dataset and Experimental Setup
The FORD LiDAR-video dataset [3] is collected by an
autonomous Ford F-250 vehicle integrated with the following
perception and navigation sensors as follows:
• Velodyne HDL-64E LiDAR with two blocks of lasers
spinning at 10 Hz and a maximum range of 120m.
• Point Grey Ladybug3 omni-directional camera system
with six 2-Mega-pixel cameras collecting video data at
8fps with 1600 × 1600 resolution.
• Two Riegl LMS-Q120 LIDAR sensors installed in the
front of the vehicle generating range and intensity data
when the laser sweeps its 80° field of view (FOV).
• Applanix POS-LV420 INS with Trimble GPS system
providing the 6 degrees of freedom (DOF) estimates at
100 Hz.
• Xsens MTi-G sensor consisting of accelerometer, gyroscope, magnetometer, integrated GPS receiver, static
pressure sensor and temperature sensor. It measures the
GPS co-ordinates of the vehicle and also provides the 3D
velocity and 3D rate of turn.

Fig. 1: Training (A to B) and testing (C to D) tracks in the
downtown Dearborn Michigan.
This dataset is generated by the vehicle while driving in and
around the Ford research campus and downtown Michigan.
The data includes feature rich downtown areas as well as
featureless empty parking lots. As shown in Figure 1, we
divided the data set into training and testing sections A to B
and C to D respectively. They were chosen in a manner that
minimizes the likelihood of contamination between training
and testing. Because of this, the direction of the light source
is never the same in the testing and training sets.
B. Optical Flow
In the area of navigation of mobile robots, optical flow has
been widely used to estimate egomotion [22], depth maps [23],
reconstruct dynamic 3D scene depth [24], and segment moving
objects [25]. Optical flow provides information of the scene
dynamics and is expressed as an estimate of velocity at each

30

CLASS 2 [x:-12,y:32]

Fig. 2: Optical flow: Hue indicates orientation and saturation
indicates magnitude

pixel from two consecutive frames, denoted by ~u and ~v . The
motion field from these two frames is measured by the motion
of the pixel brightness pattern, where the changes in image
brightness is due to the camera or object motion. [26] describes
an algorithm for computing optical flow from images, which is
used during the preprocessing step. Figure 2 shows an example
of the optical flow computed using two consecutive frames
from the Ford LiDAR-video dataset. By including optical flow
as input channels, we imbue the DCNN with information on
the dynamics observed across time steps.

Offset
along y−axis
y-axis (number
(numberof
ofpixels)
pixels)
Offset along

CLASS 3 [x:0,y:24]
20

CLASS 9 [x:-20,y:16]
10

CLASS 4 [x:16,y:8]
CLASS 1 [x:0,y:0]

0

−10

CLASS 8 [x:-16,y:-8]
CLASS 5 [x:20,y:-16]

−20

CLASS 7 [x:0,y:25]
CLASS 6 [x:12,y:-32]

−30
−30

−20

IV. M ODEL D ESCRIPTION
Our models for auto-registration are DCNNs trained to
classify the current misalignment of the LiDAR-video data
streams into one of a predefined set of offsets. DCNNs are
probably the most successful deep learning model to date on

0

10

20

30

(a) Visualization of the elliptically distributed N = 9 classes

L

LIDAR

C. Preprocessing
At each video frame timestep, the inputs to our model
consist of C channels of data with C ranging from 3-6
channels. Channels consist of grayscale Gr or (R,G,B) information from the video, horizontal and vertical components
of optical flow (U,V) and depth information L from LiDAR
The data from each modality is reshaped to a fixed size of
800 × 256 values, which are partitioned into p × p patches
at a prescribed stride. Each patch p × p is stacked across C
channels, effectively generating a vector of C dimensions. The
different preprocessing parameters are denoted by patch size
p, stride s and the number of input channels C.
Preprocessing is repeated N times, where N is the number
of offset classes. For each offset class, the video (R,G,B)
and optical flow (U,V) channels are kept static and the
depth (L) channel from the LiDAR is moved by the offset
simulating a misalignment between the video and the LiDAR
sensors. In order to accurately detect the misalignment in the
LiDAR and Video sensor data, a threshold is set to limit
the information available in each channel. The LiDAR data
has regions of sparsity and hence the LiDAR patches with a
variance (σ 2 < 15%) are dropped from the final dataset. This
leads to the elimination of the majority of foreground patches
in the data set, reducing the size of the training and testing
set by approximately 80%. Figure 3a shows a N = 9 class
elliptically distributed set of offsets and Figure 3b shows a
p × p patch stacked across all the different C channels.

−10

Offset
Offsetalong
alongx−axis
x-axis(number
(numberofofpixels)
pixels)

Optical
flow
LIDAR-Video sensor mounted
on the FORD vehicle

Video

(U,V)

(R,G,B)

RGBLUV
channels

(b) Stacking of p × p patch over all the different channels

Fig. 3: Preprocessing steps

fielded applications. The fact that the algorithm shares weights
in the training phase, results in fewer model parameters and
more efficient training. DCNNs are particularly useful for
problems in which local structure is important, such as object
recognition in images and temporal information for voice
recognition. The alternating steps of convolution and pooling
generates features at multiple scales which in turn imbues
DCNN’s with scale invariant characteristics.
The model shown in Figure 4 consists of 3 pairs of
convolution-pooling layers, that estimates the offset between
the LiDAR-video inputs at each time step. For each patch
within a timestep, there are N variants with the LiDAR-videooptical flow inputs offset by the predetermined amounts. The
CNN outputs to a softmax layer, thereby providing an offset
classification value for each patch of the frame. As described
in Section III-C, 32 × 32 patches were stacked across the
different channels and provided as the input to the DCNN.
All the 6 channels RGBLUV were used for the majority of
the experiments, whereas only 4 channels were required for
the RGBL and the GrLUV experiments. The first convolutional
layer uses 32 filters (or kernels) of size 5 × 5 × C with a stride
of 1 pixel and padding of 2 pixels on the edges. The following
pooling layer generates the input data (of size 16×16×32) for

LIDAR
Optical
flow

Video

L
(U,V)

(R,G,B)

RGBLUV
channels

5x5
convolution

32x32xC
Input
C = No. of input channels

Conv1
32 kernels
stride=1

Max
pooling

Conv2
32 kernels
stride=1

Max
pooling

Conv3
64 kernels
stride=1

Fully
Max
pooling connected
layer

Fig. 4: Experimental setup of the LiDAR-video DCNN with 5 × 5 convolution

the second convolutional layer. This layer uses 32 filters of size
5×5×32 with a stride of 1 pixel and padding of 2 pixels on the
edges. A second pooling layer, similar to the first one is used to
generate input with size 8 × 8 × 32 for the third convolutional
layer that uses 64 filters of size 5 × 5 × 32 with the stride
and padding same as previous convolutional layer. The third
pooling layer with similar configuration as the two previous
pooling layers connects to an output softmax layer with labels
corresponding to the N = 9 classes. The DCNN described
above was trained using stochastic gradient descent with a
mini-batch size of 100 epochs. The DCNN is configured with
Rectified Linear Units (ReLUs), as they train several times
faster than their equivalents with tanh connections [27]
The NVIDIA Kepler series K40 GPUs [28] are very
FLOPS/Watt efficient and are being used to drive real-time
image processing capabilities [29]. These GPUs consist of
2880 cores with 12 GB of on-board device memory (RAM).
Deep Learning applications have been targeted on GPUs
previously in [30] and these implementations are both compute
and memory bound. Stacking of channels results in a vector
of 32 × 32 × C , which is suitable for the Single Instruction
Multiple Datapath (SIMD) architecture of the GPUs. At the
same time, the training batch size caches in the GPU memory,
so the utilization of the K40 GPU’s memory is very high. This
also results in our experiments to run successfully on a single
GPU instead of partitioning the different layers over multiple
GPUs.
V. E XPERIMENTS
A. Dataset using elliptically distributed offsets
In our experiments, elliptically distributed set of N = 9
offsets of the LiDAR-video data were considered. The LiDAR
data is displaced along an ellipse with a major axis of 32 pixels
and a minor axis of 16 pixels rotated clockwise from x-axis by
45° as shown in Figure 3a. Separate training and testing sets
were generated from two different tracks as shown in Figure 1
for all the N = 9 offsets of LiDAR data. Training and testing
tracks have never seen regions and also have different lighting
conditions. Our preprocessing step described in Section III-C
results in 223, 371 and 126, 513 patches for testing and training
extracted from 469 and 224 images respectively.
In the testing phase, for each frame a simple voting scheme
is used to aggregate the patch level offset predictions to a

single frame level prediction. A sample histogram of the patch
level predictions is show in Figure 5. We color each patch of
the frame with a color corresponding to the predicted class as
shown in Figure 5.
B. Experimental results
Table I lists the inputs and CNN parameters explored ranked
in the order of increasing accuracy. We averaged the values
across the diagonal of the confusion matrix to determine the
image level and patch level accuracy. Patch level accuracy
is the individual performance of all the 32 × 32 patches
from the testing images. Classification of patches belonging
to a single time-step are voted to predict the shift for image
level accuracy. In Table I, the first 3 columns show the
results for different number of filter combinations in the
convolutional layers with fixed number of filters and input
channels RGBLUV. We observed that the image and patch
level accuracy decreased with the increase in the number of
filters. For experiments shown in columns 4 and 5, the filter
size was increased, with the number of filters constant at
(32, 32, 64). We observed that for the 6 channels RGBLUV,
filter size of 9 gave the best image level accuracy of 63.03%.
Column 6 shows the results of our experiment after dropping
the optical flow UV channels. The image and patch level
accuracy decreased for this case, indicating that optical flow
contributed significantly towards image registration. The remaining experiments utilized the Grayscale information Gr
instead of RGB and produced the best results with 76.69% and
41.05% image and patch level accuracy respectively. Table II
shows that by using information from consecutive frames the
performance increases significantly.
VI. C ONCLUSIONS AND F UTURE W ORK
In this paper, we proposed a deep learning method to do
LiDAR-Video registration. We demonstrated the effect of filter
size, number of filters and different channels. We also showed
the advantage of using temporal information, optical flow and
grayscale. The next step in taking this work forward is to
complete our development of a deep auto-registration method
for ground and aerial platforms requiring no a priori calibration
ground truth. Our aerospace applications in particular present
noisier data with an increased number of degrees of freedom.
The extension of these methods to simultaneously register

Fig. 5: Left: Histogram of the classes detected by all patches. Right: Location of patches color coded with the predicted class
TABLE I: Results of different combination of input channels C and CNN parameters
Channels
Filter size
# of filters
Image Level
accuracy(%)
Patch
Level
accuracy(%)

(32,32,32)
61.75
38.74

RGBLUV
5
(32,32,64) (64,64,64)
61.06
60.09
38.57

38.49

RGBL

61.79

GrLUV
5
9
(32,32,64)
63.03
60.66
68.03 76.69

38.03

39.00

7

9

39.28

40.96

41.05

TABLE II: Performance using temporal information
Number of consecutive timesteps used
Accuracy(%)

1

2

3

4

5

6

7

8

76.33

85.42

88.88

90.30

92.52

93.85

94.29

95.12

information across multiple platforms and larger numbers of
modalities will provide interesting challenges that we look
forward to working on.
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(a) Patch level confusion matrix (41.05% accuracy)

(b) Image level confusion matrix (76.69% accuracy)

Fig. 6: Confusion Matrix for elliptically distributed N = 9
classes using Greyscale, Optical Flow and LiDAR channels
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