This paper presents a Multiplicative Extended Kalman Filter (MEKF) framework using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for Terrain Relative Navigation (TRN) applications. The newly developed velocimeter LIDAR is capable of providing simultaneous position, Doppler velocity, and reflectivity measurements for every point in the point cloud. This information, along with pseudo-measurements from point cloud registration
techniques, a novel bulk velocity batch state estimation process and inertial measurement data, is fused within a traditional Kalman filter architecture. Results from extensive emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory and Monte Carlo simulations are presented to evaluate the efficacy of the proposed
algorithms.
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A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data
This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection.
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- NSF-PAR ID:
- 10326311
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
- Page Range / eLocation ID:
- 1287 to 1292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMC52423.2021.9658639
