An official website of the United States government
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.
more »
« less
A New Data Association Method Using Kalman Filter Innovation Vector Projections
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.
more »
« less
- Award ID(s):
- 1637899
- PAR ID:
- 10203879
- Date Published:
- Journal Name:
- 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
- Page Range / eLocation ID:
- 318 to 327
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
This research establishes new methods to quantify lidar-based navigation safety in highly automated vehicle (HAV) applications. Lidar navigation requires feature extraction (FE) and data association (DA). In prior work, an FE and DA risk prediction process was developed assuming that the set of extracted features matched the set of mapped landmarks. This paper addresses these limiting assumptions by first providing the means to select a subset of feature measurements (to be used in the estimator) while accounting for all existing landmarks in the surroundings. This is achieved by employing a probabilistic lower-bound on the mean innovation vector’s norm. This measure of landmark separation is used in an analytical integrity risk bound that accounts for all possible association hypotheses. Then, a solution separation algorithm is employed to detect unmapped obstacles and wrong extractions. The integrity risk bound is modified to incorporate the risk of not detecting an unwanted obstacle (UO) when one might be present. Covariance analysis, direct simulation, and preliminary testing show that selecting fewer extracted features can significantly reduce integrity risk, but can also decrease landmark redundancy, thereby reducing UO detection capability.more » « less
-
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.more » « less
-
Indoor localization is a challenging task. Compared to outdoor environments where GPS is dominant, there is no robust and almost-universal approach. Recently, machine learning (ML) has emerged as the most promising approach for achieving accurate indoor localization. Nevertheless, its main challenge is requiring large datasets to train the neural networks. The data collection procedure is costly and laborious, requiring extensive measurements and labeling processes for different indoor environments. The situation can be improved by Data Augmentation (DA), a general framework to enlarge the datasets for ML, making ML systems more robust and increasing their generalization capabilities. This paper proposes two simple yet surprisingly effective DA algorithms for channel state information (CSI) based indoor localization motivated by physical considerations. We show that the number of measurements for a given accuracy requirement may be decreased by an order of magnitude. Specifically, we demonstrate the algorithms’ effectiveness by experiments conducted with a measured indoor WiFi measurement dataset: As little as 10% of the original dataset size is enough to get the same performance as the original dataset. We also showed that if we further augment the dataset with the proposed techniques, test accuracy is improved more than three-fold.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PLANS46316.2020.9110229
