3,402 research outputs found
LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM
LiDAR odometry is one of the essential parts of LiDAR simultaneous
localization and mapping (SLAM). However, existing LiDAR odometry tends to
match a new scan simply iteratively with previous fixed-pose scans, gradually
accumulating errors. Furthermore, as an effective joint optimization mechanism,
bundle adjustment (BA) cannot be directly introduced into real-time odometry
due to the intensive computation of large-scale global landmarks. Therefore,
this letter designs a new strategy named a landmark map for bundle adjustment
odometry (LMBAO) in LiDAR SLAM to solve these problems. First, BA-based
odometry is further developed with an active landmark maintenance strategy for
a more accurate local registration and avoiding cumulative errors.
Specifically, this paper keeps entire stable landmarks on the map instead of
just their feature points in the sliding window and deletes the landmarks
according to their active grade. Next, the sliding window length is reduced,
and marginalization is performed to retain the scans outside the window but
corresponding to active landmarks on the map, greatly simplifying the
computation and improving the real-time properties. In addition, experiments on
three challenging datasets show that our algorithm achieves real-time
performance in outdoor driving and outperforms state-of-the-art LiDAR SLAM
algorithms, including Lego-LOAM and VLOM.Comment: 9 pages, 3 tables, 6 figure
Light-LOAM: A Lightweight LiDAR Odometry and Mapping based on Graph-Matching
Simultaneous Localization and Mapping (SLAM) plays an important role in robot
autonomy. Reliability and efficiency are the two most valued features for
applying SLAM in robot applications. In this paper, we consider achieving a
reliable LiDAR-based SLAM function in computation-limited platforms, such as
quadrotor UAVs based on graph-based point cloud association. First, contrary to
most works selecting salient features for point cloud registration, we propose
a non-conspicuous feature selection strategy for reliability and robustness
purposes. Then a two-stage correspondence selection method is used to register
the point cloud, which includes a KD-tree-based coarse matching followed by a
graph-based matching method that uses geometric consistency to vote out
incorrect correspondences. Additionally, we propose an odometry approach where
the weight optimizations are guided by vote results from the aforementioned
geometric consistency graph. In this way, the optimization of LiDAR odometry
rapidly converges and evaluates a fairly accurate transformation resulting in
the back-end module efficiently finishing the mapping task. Finally, we
evaluate our proposed framework on the KITTI odometry dataset and real-world
environments. Experiments show that our SLAM system achieves a comparative
level or higher level of accuracy with more balanced computation efficiency
compared with the mainstream LiDAR-based SLAM solutions
ViWiD: Leveraging WiFi for Robust and Resource-Efficient SLAM
Recent interest towards autonomous navigation and exploration robots for
indoor applications has spurred research into indoor Simultaneous Localization
and Mapping (SLAM) robot systems. While most of these SLAM systems use Visual
and LiDAR sensors in tandem with an odometry sensor, these odometry sensors
drift over time. To combat this drift, Visual SLAM systems deploy compute and
memory intensive search algorithms to detect `Loop Closures', which make the
trajectory estimate globally consistent. To circumvent these resource (compute
and memory) intensive algorithms, we present ViWiD, which integrates WiFi and
Visual sensors in a dual-layered system. This dual-layered approach separates
the tasks of local and global trajectory estimation making ViWiD resource
efficient while achieving on-par or better performance to state-of-the-art
Visual SLAM. We demonstrate ViWiD's performance on four datasets, covering over
1500 m of traversed path and show 4.3x and 4x reduction in compute and memory
consumption respectively compared to state-of-the-art Visual and Lidar SLAM
systems with on par SLAM performance
RadarSLAM: Radar based Large-Scale SLAM in All Weathers
Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been
presented in last decade using different sensor modalities. However, robust
SLAM in extreme weather conditions is still an open research problem. In this
paper, RadarSLAM, a full radar based graph SLAM system, is proposed for
reliable localization and mapping in large-scale environments. It is composed
of pose tracking, local mapping, loop closure detection and pose graph
optimization, enhanced by novel feature matching and probabilistic point cloud
generation on radar images. Extensive experiments are conducted on a public
radar dataset and several self-collected radar sequences, demonstrating the
state-of-the-art reliability and localization accuracy in various adverse
weather conditions, such as dark night, dense fog and heavy snowfall
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
Ego-motion estimation is a fundamental requirement for most mobile robotic
applications. By sensor fusion, we can compensate the deficiencies of
stand-alone sensors and provide more reliable estimations. We introduce a
tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing
the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO)
can perform well with acceptable drift after long-term experiment, even in
challenging cases where the lidar measurements can be degraded. Besides, to
obtain more reliable estimations of the lidar poses, a rotation-constrained
refinement algorithm (LIO-mapping) is proposed to further align the lidar poses
with the global map. The experiment results demonstrate that the proposed
method can estimate the poses of the sensor pair at the IMU update rate with
high precision, even under fast motion conditions or with insufficient
features.Comment: Accepted by ICRA 201
FLAT2D: Fast localization from approximate transformation into 2D
Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment
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