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1. A New Approach to Unwanted-Object Detection in GNSS/LiDAR-Based Navigation

   https://doi.org/10.3390/s18082740  

   Joerger, Mathieu; Arana, Guillermo Duenas; Spenko, Matthew; Pervan, Boris (August 2018, Sensors)

   In this paper, we develop new methods to assess safety risks of an integrated GNSS/LiDAR navigation system for highly automated vehicle (HAV) applications. LiDAR navigation requires feature extraction (FE) and data association (DA). In prior work, we established an FE and DA risk prediction algorithm assuming that the set of extracted features matched the set of mapped landmarks. This paper addresses these limiting assumptions by incorporating a Kalman filter innovation-based test to detect unwanted object (UO). UO include unmapped, moving, and wrongly excluded landmarks. An integrity risk bound is derived to account for the risk of not detecting UO. Direct simulations and preliminary testing help quantify the impact on integrity and continuity of UO monitoring in an example GNSS/LiDAR implementation. 

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2. Quantifying Safety of Laser-Based Navigation

   https://doi.org/10.1109/TAES.2018.2850381  

   Joerger, Mathieu; Pervan, Boris (June 2018, IEEE Transactions on Aerospace and Electronic Systems)

   In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment. 

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3. On Robot Localization Safety for Fixed-Lag Smoothing: Quantifying the Risk of Misassociation

   https://doi.org/10.1109/PLANS46316.2020.9110126  

   Hafez, Osama Abdul; Arana, Guillermo Duenas; Chen, Yihe; Joerger, Mathieu; Spenko, Matthew (April 2020, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS))

   Monitoring localization safety will be necessary to certify the performance of robots that operate in life-critical applications, such as autonomous passenger vehicles or delivery drones because many current localization safety methods do not account for the risk of undetected sensor faults. One type of fault, misassociation, occurs when a feature extracted from a mapped landmark is associated to a non-corresponding landmark and is a common source of error in feature-based navigation applications. This paper accounts for the probability of misassociation when quantifying landmark-based mobile robot localization safety for fixed-lag smoothing estimators. We derive a mobile robot localization safety bound and evaluate it using simulations and experimental data in an urban environment. Results show that localization safety suffers when landmark density is relatively low such that there are not enough landmarks to adequately localize and when landmark density is relatively high because of the high risk of feature misassociation. 

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4. A New Data Association Method Using Kalman Filter Innovation Vector Projections

   https://doi.org/10.1109/PLANS46316.2020.9110229  

   Joerger, Mathieu; Hassani, Ali (April 2020, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS))

   null (Ed.)

   This paper describes the derivation, analysis and implementation of a new data association method that provides a tight bound on the risk of incorrect association for LiDAR feature-based localization. Data association (DA) is the process of assigning currently-sensed features with ones that were previously observed. Most DA methods use a nearest-neighbor criterion based on the normalized innovation squared (NIS). They require complex algorithms to evaluate the risk of incorrect association because sensor state prediction, prior observations, and current measurements are uncertain. In contrast, in this work, we derive a new DA criterion using projections of the extended Kalman filter's innovation vector. The paper shows that innovation projections (IP) are signed quantities that not only capture the impact of an incorrect association in terms of its magnitude, but also of its direction. The IP-based DA criterion also leverages the fact that incorrect associations are known and well-defined fault modes. Thus, as compared to NIS, IPs provide a much tighter bound on the predicted risk of incorrect association. We analyze and evaluate the new IP method using simulated and experimental data for autonomous inertial-aided LiDAR localization in a structured lab environment. 

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5. Echolocating bats inspect and discriminate landmark features to guide navigation

   https://doi.org/doi:10.1242/jeb.191965  

   Yu, C.; Luo, J.; Wohlgemuth, M.J.; Moss, C.F. (April 2019, Journal of experimental biology)

   Landmark-guided navigation is a common behavioral strategy for way-finding, yet prior studies have not examined how animals collect sensory information to discriminate landmark features. We investigated this question in animals that rely on active sensing to guide navigation. Four echolocating bats (Eptesicus fuscus) were trained to use an acoustic landmark to find and navigate through a net opening for a food reward. In experimental trials, an object serving as a landmark was placed adjacent to a net opening and an object serving as a distractor was placed next to a barrier (covered opening). The location of the opening, barrier and objects were moved between trials, but the spatial relationships between the landmark and opening, and between the distractor and barrier were maintained. In probe trials, the landmark was placed next to a barrier, while the distractor was placed next to the opening, to test whether the bats relied on the landmark to guide navigation. Vocal and flight behaviors were recorded with an array of ultrasound microphones and high-speed infrared motion-capture cameras. All bats successfully learned to use the landmark to guide navigation through the net opening. Probe trials yielded an increase in both the time to complete the task and the number of net crashes, confirming that the bats relied largely on the landmark to find the net opening. Further, landmark acoustic distinctiveness influenced performance in probe trials and sonar inspection behaviors. Analyses of the animals’ vocal behaviors also revealed differences between call features of bats inspecting landmarks compared with distractors, suggesting increased sonar attention to objects used to guide navigation. 

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