RTISSLAM: In-air Imaging Sonar SLAM using a Robust Pose Graph and Occupancy Grid Mapping

Jan Steckel

cs.ROOct 13, 2026

RTISSLAM: In-air Imaging Sonar SLAM using a Robust Pose Graph and Occupancy Grid Mapping

Authors: Jan Steckel

Organizations: Cosys-Lab, Faculty of Applied Engineering, University of Antwerp, 2020 Antwerp, Belgium · Flanders Make Strategic Research Centre, 3920 Lommel, Belgium

Abstract

Sonar was one of the first sensors with which mobile robots built maps, and in-air sonar keeps working where cameras and lidar fail, in darkness, smoke, dust and next to glass. Classical sonar SLAM, however, relied on geometric features or occupancy grids built from sparse range readings, and both struggle with the specular reflections that dominate indoor sonar. In this letter, we introduce RTISSLAM, a novel sonar-only SLAM system for an embedded 3D imaging sonar, which combines the robust pose graph of BatSLAM 2.0 as back-end with an occupancy grid as map. Energyscapes from delay-multiply-and-sum beamforming with a range-wise CFAR serve as place descriptors for sequence-verified loop closures, acoustic odometry calibrates the wheel odometry online, recognized revisits are registered metrically, and the grid is stored per keyframe and anchored to the pose-graph nodes, so that it follows every loop closure. In simulated worlds with specular walls, RTISSLAM reduces the trajectory error of the wheel odometry from \SI{8.2}{\meter} to \SI{0.31}{\meter} in an office, while lidar-style grid SLAM on the same sonar data fails.

Figures & tables

Panel of Figure 1 — Fig. 1: Overview of the processing flow of RTISSLAM. Every pulse of the eRTIS is beamformed into an energyscape $P(r,\psi)$ , which feeds the place recognition of BatSLAM 2.0 (local views, templates and the sequence verifier), the acoustic odometry and the keyframe evidence of the occupancy grid. The acoustic odometry calibrates the wheel odometry ( $\kappa_{s}$ , $\kappa_{\theta}$ ), whose corrected increments enter the pose graph. Committed recognitions are registered metrically on the echoes of both visits before they enter the pose graph as loop closure links. The occupancy grid is rendered from the keyframe evidence, placed with the current estimate $\hat{X}$ of the pose graph, so that it follows every loop closure; it does not feed back into the pose estimate.

Fig. 2: The Three Simulated Worlds: the office, the warehouse and the atrium. Grey: walls and furniture footprints; blue: reflector points at sensor height; red triangles: concave corners; orange squares: door frames; purple: point objects; green: the mapping tour, starting at the circle.

Fig. 3: Energyscapes at Three Poses in the Office, in the sensor frame (forward up), range-compensated ( $+40\log_{10}r$ ) and normalized per image, with the colormap from $-30\text{\,}\mathrm{dB}$ to $0\text{\,}\mathrm{dB}$ . Left: the scene; then Delay-And-Sum (DAS), third-order Delay-Multiply-And-Sum with Coherence Factor (DMAS3 + CF), and DMAS3 + CF with the Range-Wise CFAR used by RTISSLAM. Grid lines every $30\text{\,}\mathrm{\SIUnitSymbolDegree}$ and $1\text{\,}\mathrm{m}$ .

Variant	Office	Warehouse	Atrium
Wheel odometry	8.20 $\,\pm\,$ 0.33	5.09 $\,\pm\,$ 0.35	2.66 $\,\pm\,$ 0.34
+ AO calibration	2.32 $\,\pm\,$ 0.47	1.48 $\,\pm\,$ 0.25	2.99 $\,\pm\,$ 0.61
BatSLAM 2.0 links	0.46 $\,\pm\,$ 0.06	0.82 $\,\pm\,$ 0.10	1.03 $\,\pm\,$ 0.07
+ registration	0.41 $\,\pm\,$ 0.05	0.91 $\,\pm\,$ 0.12	0.53 $\,\pm\,$ 0.10
+ AO calib. (RTISSLAM)	0.31 $\,\pm\,$ 0.13	0.57 $\,\pm\,$ 0.13	1.78 $\,\pm\,$ 0.32
slam_toolbox, stock	23.1 $\,\pm\,$ 8.2	35.5 $\,\pm\,$ 9.4	17.3 $\,\pm\,$ 2.4

TABLE I: Absolute Trajectory Error After the Two-Lap Drives (m, Mean

\,\pm\,

Standard Deviation Over Five Seeds).

Fig. 4: RTISSLAM and slam_toolbox on the Three Worlds (First Seed). Top: ground truth (black), wheel odometry (red), slam_toolbox (green) and RTISSLAM (blue), with their ATE, on the floor plan (reference only). Middle: the RTISSLAM occupancy grid, rendered from the final pose graph, with the estimated trajectory, its template nodes (blue dots) and the nodes at which a revisit was recognized (orange). Bottom: the slam_toolbox grid and trajectory on the same sonar echoes and odometry. Grids: black is occupied, white is free and grey is unknown.

Fig. 5: The Online Occupancy Grid of the Office Just Before and After a Loop Closure. Dashed black: ground truth (reference only); blue: online RTISSLAM estimate; magenta: current node. The keyframes are placed with the current estimate of their anchor nodes, so that the loop closure moves their evidence with them.

Fig. 6: Occupancy Grids per Imaging Method in the Three Worlds, rendered from the final RTISSLAM poses (first seed) with the same inverse sensor model: Delay-And-Sum (DAS) and DMAS3 + CF, each without and with the Range-Wise CFAR. The configuration of RTISSLAM is highlighted.

Fig. 7: Acoustic Odometry on the Office Drive (First Seed). a) Convergence of the wheel calibration (dotted: the true correction factors). b) Accumulated heading error of the raw wheel odometry, the acoustic odometry (wheel increments where an estimate was rejected) and the calibrated wheel odometry.

Occupancy Grid Mapping

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RTISSLAM-3D: Full 6-DoF SLAM With an In-Air 3D Imaging Sonar
Oct 15, 2026Jan Steckel, Vincent Katter, Wouter JansenSimultaneous Localization and Mapping Robot Localization
Cosys-Lab, Faculty of Applied Engineering, University of Antwerp, 2020 Antwerp, Belgium · Flanders Make Strategic Research Centre, 3920 Lommel, Belgium
BatSLAM 2.0: Sequence-Verified Sonar Place Recognition in a Robust Pose Graph
Sep 30, 2026Jan SteckelSimultaneous Localization and Mapping Loop Closure Detection
Cosys-Lab, Faculty of Applied Engineering, University of Antwerp, 2020 Antwerp, Belgium · Flanders Make Strategic Research Centre, 3920 Lommel, Belgium
SOL-SLAM: Inverse Compositional Gauss-Newton Direct Registration for Fast Sonar-Only Local SLAM
Sep 16, 2026Kalvik Jakkala, Jason O'KaneSimultaneous Localization and Mapping Underwater Robotics
Texas A&M University

Oct 15, 2026J/K move · Enter open · S save

RTISSLAM-3D: Full 6-DoF SLAM With an In-Air 3D Imaging Sonar

Jan Steckel, Vincent Katter, Wouter Jansen

Cosys-Lab, Faculty of Applied Engineering, University of Antwerp, 2020 Antwerp, Belgium · Flanders Make Strategic Research Centre, 3920 Lommel, Belgium

Simultaneous Localization and Mapping Robot Localization

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Echolocating bats navigate dense forests using sonar alone, and in-air 3D imaging sonars now capture the entire frontal hemisphere with every pulse. Sonar SLAM, however, has so far been planar: current systems estimate a 2D trajectory and discard the elevation information in the sonar images, an assumption that does not hold on rough and sloped terrain such as a forest floor. In this paper, we introduce RTISSLAM-3D, a novel 6-DoF SLAM system for a wheeled robot that carries an in-air 3D imaging sonar, an IMU and wheel encoders. A CFAR detector on the sphere reduces every sonar image to a sparse set of 3D detections, which we use for a 3D acoustic odometry that models the shape of the sonar beam, for place recognition with polar submap descriptors, and for the verification of loop closures by 3D registration, all combined in a 6-DoF pose graph with attitude factors. We evaluated RTISSLAM-3D on a 545m drive through a forest, with a 3D lidar SLAM as reference. The loop closures reduced the horizontal error of the acoustic odometry from 1.74m to 0.28m, with a height error of 0.12m, and 94.6% of the resulting 3D sonar map lies within 0.3 m of the lidar map.

Abstract

Occupancy Grid Mapping

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