LION: Lidar-Inertial Observability-Aware Navigator for Vision-Denied Environments

Tagliabue, Andrea; Tordesillas, Jesus; Cai, Xiaoyi; Santamaria-Navarro, Àngel; How, Jonathan P.; Carlone, Luca; Agha–mohammadi, Ali–akbar

doi:10.1007/978-3-030-71151-1_34

Cited by 47 publications

(25 citation statements)

References 29 publications

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“…As the robot navigates the featureless corridor, in one section of the trajectory a significant drop can be seen in the values of the smallest eigenvalue of the Hessian, while variations in the rest of eigenvalues are minimal. This leads to an increase in the values of the condition number in (12) as shown in the plot of log(κ) values. In this experiment, it was possible to verify that increased values of log(κ) corresponded to noisy lidar-based odometric estimates based on the wheel-inertial odometry.…”

Section: Determination Of Geometric Degeneracymentioning

confidence: 95%

“…that can be used for global localization in the pre-matching step. By relying on the degeneracy metric κ in (12), the level of observability of the scene can be analyzed in real-time to remove ambiguous regions as shown in Fig. 11-(a) from loop closure consideration.…”

Section: Visual Saliency In Occupancy Grid Mapsmentioning

confidence: 99%

“…SLAM algorithms aim to recover the most probable robot trajectory and map of the environment using the onboard sensing system. Many solutions have been proposed based on different sensing modalities, including vision [7], visual-inertial [8][9][10], thermal-inertial [11], lidar-inertial [12], range-based SLAM [13], or combination of them [14,15]. Vision-based localization and place recognition is a popular and well studied problem in the robotics and computer vision literature [16], and while great progress has been made in visual loop closure detection [17,18], vision-based methods are faced with many challenges in perceptuallydegraded environments including reduced visibility (i.e., inconsistent illumination or lackthereof, fog, and dust), and change in the appearance, caused by viewing angle variations between different visits.…”

Section: Introductionmentioning

confidence: 99%

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DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

Ebadi¹,

Palieri²,

Wood³

et al. 2021

Preprint

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Enabling fully autonomous robots capable of navigating and exploring largescale, unknown and complex environments has been at the core of robotics research for several decades. A key requirement in autonomous exploration is building accurate and consistent maps of the unknown environment that can be used for reliable navigation. Loop closure detection, the ability to assert that a robot has returned to a previously visited location, is crucial for consistent mapping as it reduces the drift caused by error accumulation in the estimated robot trajectory. Moreover, in multi-robot systems, loop closures enable merging local maps obtained by a team of robots into a consistent global map of the environment. In this paper, we present a degeneracy-aware and drift-resilient loop closing method to improve place recognition and resolve 3D location ambiguities for simultaneous localization and mapping (SLAM) in GPS-denied, large-scale and perceptually-degraded environments. More specifically, we focus on SLAM in subterranean environments (e.g., lava tubes, caves, and mines) that represent examples of complex and ambiguous environments where current methods have inadequate performance. The first contribution of this paper is a degeneracyaware lidar-based SLAM front-end to determine the observability and level of geometric degeneracy in an unknown environment. Using this crucial capability, ambiguous and unobservable areas in an unknown environment are determined and excluded from the search for loop closures to avoid distortions of the entire map as the result of spurious or inaccurate loop closures. The second contribution of this paper is a drift-resilient loop closing pipeline that exploits the salient 2D and 3D features extracted from lidar point cloud data to enable a

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Section: Determination Of Geometric Degeneracymentioning

confidence: 95%

Section: Visual Saliency In Occupancy Grid Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

Ebadi¹,

Palieri²,

Wood³

et al. 2021

Preprint

Self Cite

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“…Hence, this method can improve accuracy to some extent. LION [46] can self-assess its performance using an observability metric that evaluates whether pose estimation is geometrically ill-constrained. It is similar to LODegeneracy [45] and is applied to a real tunnel scene LO.…”

Section: Sensitivity Modelmentioning

confidence: 99%

Observation Contribution Theory for Pose Estimation Accuracy

Wan¹,

He²,

Cui³

et al. 2021

Preprint

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The improvement of pose estimation accuracy is currently the fundamental problem in mobile robots. This study aims to improve the use of observations to enhance accuracy. The selection of feature points affects the accuracy of pose estimation, leading to the question of how the contribution of observation influences the system. Accordingly, the contribution of information to the pose estimation process is analyzed. Moreover, the uncertainty model, sensitivity model, and contribution theory are formulated, providing a method for calculating the contribution of every residual term. The proposed selection method has been theoretically proven capable of achieving a global statistical optimum. The proposed method is tested on artificial data simulations and compared with the KITTI benchmark. The experiments revealed superior results in contrast to ALOAM and MLOAM. The proposed algorithm is implemented in LiDAR odometry and LiDAR Inertial odometry both indoors and outdoors using diverse LiDAR sensors with different scan modes, demonstrating its effectiveness in improving pose estimation accuracy. A new configuration of two laser scan sensors is subsequently inferred. The configuration is valid for three-dimensional pose localization in a prior map and yields results at the centimeter level.

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“…Heterogeneous Resilient State Estimation: These sensors signals are process by our estimation module HeRO (Heterogeneous Redundant Odometry) [15] that further adds additional analytical redundancy by running multiple odometry algorithms [18], [19], [20], performs confidence checks to quantify uncertainty and finally produces robust pose estimates by multiplexing to the most robust odometry source. This provides resiliency to state estimation failures which are usually triggered by many factors like dust, low-light conditions, lack of features/landmarks, motion blur and issues with dynamic range.…”

Section: Resilient Multi-fidelity Architecturementioning

confidence: 99%

Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

Thakker¹,

Alatur²,

Fan³

et al. 2021

Preprint

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We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages. Environments are GPS-denied and perceptuallydegraded with variable lighting from dark to lit and obscurants (dust, fog, smoke). Lack of prior maps and degraded communication eliminates the possibility of prior or off-board computation or operator intervention. This necessitates real-time onboard computation using noisy sensor data. To address these challenges, we propose a resilient architecture that exploits redundancy and heterogeneity in sensing modalities. Further resilience is achieved by triggering recovery behaviors upon failure. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots, as a part of Team CoSTAR's effort to the DARPA Subterranean Challenge, where the team won 2 nd and 1 st place in the Tunnel and Urban Circuits, respectively.

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LION: Lidar-Inertial Observability-Aware Navigator for Vision-Denied Environments

Cited by 47 publications

References 29 publications

DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

Observation Contribution Theory for Pose Estimation Accuracy

Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

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