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

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

Jan Steckel, Vincent Katter, Wouter Jansen · N/A · 2026

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Summary

Echolocating bats navigate dense forests using sonar alone, and in-air 3D imaging sonars now capture the entire frontal hemisphere with every pulse.

Abstract Summary

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.

Key Points

  • 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

Overview of RTISSLAM-3D

Figure 1 shows the processing flow of RTISSLAM-3D. For every pulse, the eRTIS records the echoes with its $N=32$ microphones, which are beamformed with fifth-order DMAS with coherence factor (CF) into an energy volume $P(r,\psi)$ over $M=2293$ directions $\psi$ that sample the frontal hemisphere uniformly (section III-A ).

Overview of RTISSLAM-3D — RTISSLAM-3D: Full 6-DoF SLAM With an In-Air 3D Imaging Sonar
Fig. 2: 3D Detections of a Single eRTIS Capture in the Forest. Panel a) shows the view of the robot’s camera (for illustration only). Panel b) shows the acoustic image of the capture (DMAS5 with coherence factor, maximum over range, $40\text{\,}\mathrm{dB}$ dynamic range) over the frontal hemisphere as seen from behind the sensor, with circles at $30\text{\,}\mathrm{\SIUnitSymbolDegree}$ and $60\text{\,}\mathrm{\SIUnitSymbolDegree}$ off the sensor axis, and the directions of the CFAR detections (white circles; cyan crosses: ground mirrors). Panels c) and d) show the 3D detections in the sensor frame, in a side view and a top view, colored by their height above the fitted ground plane, on top of the lidar scan of the same instant (dark gray: lidar returns above the ground, light gray: ground). Red crosses: detections removed as ground multipath mirrors, i.e., more than $0.15\text{\,}\mathrm{m}$ below the fitted ground plane (dashed line in panel c)). The sonar sees the ground in front of the robot and the near sides of the trunks.

Method

In this section, we detail the components of RTISSLAM-3D in the order of figure 1 . We use the notation of RTISSLAM [ 13 ] , extended to 3D: the eRTIS has $N$ microphones at positions $p_{y}(i)$, $p_{z}(i)$ in its $YZ$-plane, and a direction $\psi$ has azimuth $\theta$ and elevation $\varphi$, with unit vector $\vec{u}(\psi)$ in the sensor frame ($x$ forward, $y$ left, $z$ up).

The planar array of the eRTIS cannot distinguish echoes arriving front from back, so the frontal hemisphere holds all the directional information of a pulse.

Method — RTISSLAM-3D: Full 6-DoF SLAM With an In-Air 3D Imaging Sonar
Fig. 3: 3D Acoustic Odometry Against the Lidar Reference. Panel a) shows the horizontal RPE (Relative Pose Error) as a function of the segment length, for the wheels and gyroscope alone (gray), the acoustic odometry in which the sonar estimates the full translation (green), and the final 3D acoustic odometry, in which the sonar estimates the horizontal translation only (orange). Panel b) shows the height RPE. Panel c) shows the dead-reckoning trajectories of the wheels and gyroscope and of the 3D acoustic odometry after a 4-DoF fit on the lidar reference (black). The sonar reduces the horizontal drift of the wheels by about $40\text{\,}\mathrm{\%}$, equally in both variants; estimating the vertical motion with the sonar reduces the height drift of the odometry alone, but not the error of the final SLAM trajectory (see text).

Experimental Setup

In this section, we describe the robot and the recording, the reference trajectory and map against which we evaluate RTISSLAM-3D, and the metrics we use.

The data were recorded with a Clearpath Husky A200 that carried an eRTIS 3D imaging sonar [ 5 , 6 ] at the front, about $0.32\text{\,}\mathrm{m}$ above the ground, together with an Ouster OS1 64 beam 3D lidar with a built-in 6-axis IMU ($100\text{\,}\mathrm{Hz}$), the wheel encoders ($11.5\text{\,}\mathrm{Hz}$), and a GoPro HERO13 camera (which we only use for illustration).

Experimental Setup — RTISSLAM-3D: Full 6-DoF SLAM With an In-Air 3D Imaging Sonar
Fig. 4: 3D Place Recognition. Panel a) shows the polar descriptors (rings from 1 to $8\text{\,}\mathrm{m}$, 60 sectors, the arrow marks the heading) of a query keyframe and of the keyframe it was matched with, for a loop closure with the median error. Panel b) shows the above-ground points of the two keyframe submaps after the 4-DoF registration (gray: matched keyframe, magenta: query). Panel c) shows the distribution (logarithmic scale) of the inlier fraction of the above-ground points after registration, for all top-five candidates of all keyframes without a position gate, split into candidates that were registered to within $0.5\text{\,}\mathrm{m}$ and $5\text{\,}\mathrm{\SIUnitSymbolDegree}$ of the reference (magenta) and the others (gray); the dashed line is the acceptance threshold.

Results

In this section, we will first look at the 3D detections of single captures. Next, we evaluate the 3D acoustic odometry and the 3D place recognition on their own, after which we present the trajectory of the complete system together with an ablation of its components and a sensitivity analysis of its thresholds. We conclude with the 3D map and the computation times.

Figure 2 shows a typical capture, taken on a forest path with trunks at 2 to $5\text{\,}\mathrm{m}$, next to the lidar scan of the same moment.

Results — RTISSLAM-3D: Full 6-DoF SLAM With an In-Air 3D Imaging Sonar
Fig. 5: RTISSLAM-3D Against the 3D Lidar Reference. Panel a) shows the trajectories after a 4-DoF fit: the reference (black), the 3D acoustic odometry without loop closures (orange) and RTISSLAM-3D (blue); magenta circles mark the query keyframes of its 233 loop closures. Panel b) shows the height along the drive. Panels c) and d) show the horizontal and the height error of both trajectories on a logarithmic scale; the dashed lines are the RMSE of RTISSLAM-3D.

Sources and Demonstrations

Figures are reproduced from Steckel et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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