4 research outputs found
Photometric LiDAR and RGB-D Bundle Adjustment
The joint optimization of the sensor trajectory and 3D map is a crucial
characteristic of Simultaneous Localization and Mapping (SLAM) systems. To
achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now
retain higher resolutions that enable the creation of point cloud images
resembling those taken by conventional cameras. Nevertheless, the typical
effective global refinement techniques employed for RGB-D sensors are not
widely applied to LiDARs. This paper presents a novel BA photometric strategy
that accounts for both RGB-D and LiDAR in the same way. Our work can be used on
top of any SLAM/GNSS estimate to improve and refine the initial trajectory. We
conducted different experiments using these two depth sensors on public
benchmarks. Our results show that our system performs on par or better compared
to other state-of-the-art ad-hoc SLAM/BA strategies, free from data association
and without making assumptions about the environment. In addition, we present
the benefit of jointly using RGB-D and LiDAR within our unified method. We
finally release an open-source CUDA/C++ implementation.Comment: 11 pages, 9 figure
Enhancing LiDAR performance: Robust De-skewing Exclusively Relying on Range Measurements
Most commercially available Light Detection and Ranging (LiDAR)s measure the
distances along a 2D section of the environment by sequentially sampling the
free range along directions centered at the sensor's origin. When the sensor
moves during the acquisition, the measured ranges are affected by a phenomenon
known as "skewing", which appears as a distortion in the acquired scan. Skewing
potentially affects all systems that rely on LiDAR data, however, it could be
compensated if the position of the sensor were known each time a single range
is measured. Most methods to de-skew a LiDAR are based on external sensors such
as IMU or wheel odometry, to estimate these intermediate LiDAR positions. In
this paper, we present a method that relies exclusively on range measurements
to effectively estimate the robot velocities which are then used for
de-skewing. Our approach is suitable for low-frequency LiDAR where the skewing
is more evident. It can be seamlessly integrated into existing pipelines,
enhancing their performance at a negligible computational cost.Comment: 6 pages , 5 figure
CaLib: Simple and Accurate LiDAR-RGB Calibration using Small Common Markers
In many fields of robotics, knowing the relative position and orientation
between two sensors is a mandatory precondition to operate with multiple
sensing modalities. In this context, the pair LiDAR-RGB cameras offer
complementary features: LiDARs yield sparse high quality range measurements,
while RGB cameras provide a dense color measurement of the environment.
Existing techniques often rely either on complex calibration targets that are
expensive to obtain, or extracted virtual correspondences that can hinder the
estimate's accuracy. In this paper we address the problem of LiDAR-RGB
calibration using typical calibration patterns (i.e. A3 chessboard) with
minimal human intervention. Our approach exploits the planarity of the target
to find correspondences between the sensors measurements, leading to features
that are robust to LiDAR noise.
Moreover, we estimate a solution by solving a joint non-linear optimization
problem. We validated our approach by carrying on quantitative and comparative
experiments with other state-of-the-art approaches. Our results show that our
simple schema performs on par or better than other approches using complex
calibration targets. Finally, we release an open-source C++ implementation at
\url{https://github.com/srrg-sapienza/ca2lib}Comment: 7 pages, 10 figure