Multi-sensor object tracking performance limits by the Cramer-Rao lower bound

Domhof, Joris; Happee, Riender; Jonker, Pieter

doi:10.23919/icif.2017.8009637

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…In this section, we furnish the reader with further insight into the proposed fusion method and compare the accuracy with other purely LiDAR-based method using the Cramér-Rao Lower Bound (CRLB). The CRLB is a lower bound on the variance of an estimator, which is often used to evaluate the performance of the data fusion method [21] [22]. Following [23], the CRLB is calculated by taking inverse of the Fisher information matrix as:…”

Section: Cram éRa-rao Lower Bound Theoremmentioning

confidence: 99%

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

Fuzhang~Han¹,

Han~Zheng²,

Wenjun~Huang³

et al. 2023

Preprint

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The fusion scheme is crucial to the multi-sensor fusion method that is the promising solution to the state estimation in complex and extreme environments like underground mines and planetary surfaces. In this work, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the CRLB theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves realtime and high estimation accuracy performance despite the challenging environment and poor observations.

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Section: Cram éRa-rao Lower Bound Theoremmentioning

confidence: 99%

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

Fuzhang~Han¹,

Han~Zheng²,

Wenjun~Huang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [4] authors give a comprehensive evaluation of tracking performance for various single and multi-sensor system setups in urban and highway scenarios. Their analysis is based on the Cramer-Rao Lower Bound (CRLB) which provides a lower bound on the variance of an estimator.…”

Section: Related Workmentioning

confidence: 99%

People Tracking by Cooperative Fusion of RADAR and Camera Sensors

Dimitrievski

Jacobs

Veelaert

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Accurate 3D tracking of objects from monocular camera poses challenges due to the loss of depth during projection. Although ranging by RADAR has proven effective in highway environments, people tracking remains beyond the capability of single sensor systems. In this paper, we propose a cooperative RADAR-camera fusion method for people tracking on the ground plane. Using average person height, joint detection likelihood is calculated by back-projecting detections from the camera onto the RADAR Range-Azimuth data. Peaks in the joint likelihood, representing candidate targets, are fed into a Particle Filter tracker. Depending on the association outcome, particles are updated using the associated detections (Tracking by Detection), or by sampling the raw likelihood itself (Tracking Before Detection). Utilizing the raw likelihood data has the advantage that lost targets are continuously tracked even if the camera or RADAR signal is below the detection threshold. We show that in single target, uncluttered environments, the proposed method entirely outperforms camera-only tracking. Experiments in a real-world urban environment also confirm that the cooperative fusion tracker produces significantly better estimates, even in difficult and ambiguous situations.

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“…The positive impact of adding Doppler measurements to the tracker in 3D space was shown in [16]. Ignoring the impact of ego-motion, the theoretical CRLB was computed in [17] for various single and multi-sensor system setups in urban and highway scenarios in order to select sensor modalities, sensor type and sensor placement for automated driving. To the best of our knowledge, a CRLB expression taking the movement of the radar into account is not available from the literature.…”

Section: Introductionmentioning

confidence: 99%

On the Accuracy of Automotive Radar Tracking

Jacobs,

Veelaert,

Steendam

et al. 2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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Radar has become a key sensor in many advanced driver assistance systems (ADAS). Due to its excellent range and Doppler resolution, low cost, and robustness against environmental conditions, it is considered an attractive sensor for detecting and tracking vulnerable road users (VRUs). In this paper, we provide a theoretical analysis of the accuracy of radar based VRU detection and tracking systems, thereby focusing on a number of specific scenarios such as early detection and tracking of VRUs, a VRU crossing the road at constant speed in front of a vehicle, a VRU moving parallel to a vehicle, etc. More specifically, we derive the Cramer-Rao lower bound (CRLB) for position and velocity estimation of a moving target based on a sequence of noisy range, azimuth, and Doppler measurements taken from a moving ego-vehicle equipped with one or multiple radars. Not only does the CRLB serve as a benchmark to evaluate the performance of any practical tracking algorithm, it also allows to gain practical insights regarding the impact of the radar setup and configuration on the tracking accuracy in different realistic scenarios. Furthermore, we show that the generalized least-squares estimator (GLSE) achieves excellent performance when few measurements are available, and propose a novel active sensing (AS) application based on the CRLB where the radar configuration is optimized on the fly to improve either tracking accuracy or computational efficiency.

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Multi-sensor object tracking performance limits by the Cramer-Rao lower bound

Cited by 7 publications

References 15 publications

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

People Tracking by Cooperative Fusion of RADAR and Camera Sensors

On the Accuracy of Automotive Radar Tracking

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