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Abstract: Robustness in Simultaneous Localization and Mapping (SLAM) remains one of the key challenges for the real-world deployment of autonomous systems. SLAM research has seen significant progress in the last two and a half decades, yet many state-of-the-art (SOTA) algorithms still struggle to perform reliably in real-world environments. There is a general consensus in the research community that we need challenging real-world scenarios which bring out different failure modes in sensing modalities. In this paper, we present a novel multi-modal indoor SLAM dataset covering challenging common scenarios that a robot will encounter and should be robust to. Our data was collected with a mobile robotics platform across multiple floors at Northeastern University's ISEC building. Such a multi-floor sequence is typical of commercial office spaces characterized by symmetry across floors and, thus, is prone to perceptual aliasing due to similar floor layouts. The sensor suite comprises seven global shutter cameras, a high-grade MEMS inertial measurement unit (IMU), a ZED stereo camera, and a 128-channel high-resolution lidar. Along with the dataset, we benchmark several SLAM algorithms and highlight the problems faced during the runs, such as perceptual aliasing, visual degradation, and trajectory drift. The benchmarking results indicate that parts of the dataset work well with some algorithms, while other data sections are challenging for even the best SOTA algorithms.
The dataset is available at the following link
| Label | Size (GB) | Duration (s) | Appx. Length (m) | Description |
|---|---|---|---|---|
| full_sequence | 515.0 | 1539.70 | 782 | reflective surfaces, minimal dynamic content, daylight, symmetric floors, elevators, open atrium |
| 5th_floor | 145.8 | 437.86 | 187 | one loop, one out and back |
| transit_5_to_1 | 36.8 | 109.00 | N/A | transit from 5th to 1st floor in middle elevator |
| 1st_floor | 43.0 | 125.58 | 65 | one loop, open layout different from other floors, many exterior windows |
| transit_1_to_4 | 112.4 | 337.40 | 144 | transit across 1st floor, up to 3rd floor in freight elevator, across 3rd floor, up to 4th floor in right elevator |
| 4th_floor | 43.2 | 131.00 | 66 | one loop, some dynamic content towards end |
| transit_4_to_2 | 21.9 | 65.00 | 22 | transit from 4th floor to second floor in right elevator |
| 2nd_floor | 89.7 | 266.00 | 128 | two loops in a figure eight |
| transit_2_to_5 | 22.2 | 65.86 | 128 | transit from 2nd floor to fifth floor in right elevator |
| Name | Description |
|---|---|
| cams_calib.yaml | calibration of front 5 cameras, including intrinsics and relative transformation between them |
| cam2_imu_calib.yaml | transformation between camera_2 and IMU, including time shift between camera_2 and IMU |
| cam5_imu_calib.yaml | calibration of left side camera and transformation between camera_5 and IMU, including time shift between camera_5 and IMU |
| cam6_imu_calib.yaml | calibration of right side camera and transformation between camera_6 and IMU, including time shift between camera_6 and IMU |
| imu_params.yaml | IMU parameters, including noise parameters of accelerometer and gyroscope, as well as sampling rate |
| Label | Size (GB) | Duration (s) | Appx. Length (m) | Description |
|---|---|---|---|---|
| full_sequence | 573.5 | 1,700.6 | 699 | feature rich rooms, featureless hallways, many obstacles, stationary and dynamic people in scene |
| 1st_floor | 144.6 | 428.70 | 221 | two loops with shared segment, some dynamic content |
| transit_1_to_3 | 28.3 | 84.00 | N/A | transit from 1st floor to 3rd floor in left elevator |
| 3rd_floor | 213.7 | 633.59 | 345 | two concentric loops with two shared segments, narrow corridor with dynamic content, near field obstructions |
| transit_3_to_2 | 27.8 | 82.41 | N/A | transit from 3rd floor to 2nd floor in right elevator |
| 2nd_floor | 126.1 | 374.00 | 186 | one loop, out and back in featureless corridor |
| transit_2_to_1 | 33.0 | 97.90 | N/A | transit from 2nd floor to 1st floor in right elevator, dynamic objects cover FOV near end |
| Name | Description |
|---|---|
| cams_calib.yaml | calibration of front 5 cameras, including intrinsics and relative transformation between them |
| cam2_imu_calib.yaml | transformation between camera_2 and IMU, as well as time shift between camera_2 and IMU |
| imu_params.yaml | IMU parameters, including noise parameters of accelerometer and gyroscope, as well as sampling rate |
| Name | Description |
|---|---|
| zed_cam2_calib.yaml | calibration of Zed cameras, including intrinsics and relative transformation with respect to camera_2 of the front 5 cameras |
| zed_imu_params.yaml | Zed IMU parameters, including noise parameters of accelerometer and gyroscope, as well as transformation with respect to the body frame |
If you use our work, please cite our paper:
P. Kaveti et al., "Challenges of Indoor SLAM: A Multi-Modal Multi-Floor Dataset for SLAM Evaluation" 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, 2023, pp. 1-8, doi: 10.1109/CASE56687.2023.10260618.
@INPROCEEDINGS{10260618,
author={Kaveti, Pushyami and Gupta, Aniket and Giaya, Dennis and Karp, Madeline and Keil, Colin and Nir, Jagatpreet and Zhang, Zhiyong and Singh, Hanumant},
booktitle={2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)},
title={Challenges of Indoor SLAM: A Multi-Modal Multi-Floor Dataset for SLAM Evaluation},
year={2023},
volume={},
number={},
pages={1-8},
doi={10.1109/CASE56687.2023.10260618}}