├── README.md ├── data ├── s6 │ ├── L_line │ │ ├── 1.csv │ │ ├── 10.csv │ │ ├── 11.csv │ │ ├── 2.csv │ │ ├── 3.csv │ │ ├── 4.csv │ │ ├── 5.csv │ │ ├── 6.csv │ │ ├── 7.csv │ │ └── 8.csv │ ├── L_shape │ │ └── test │ │ │ ├── 1.csv │ │ │ ├── 10.csv │ │ │ ├── 2.csv │ │ │ ├── 3.csv │ │ │ ├── 4.csv │ │ │ ├── 5.csv │ │ │ ├── 6.csv │ │ │ ├── 7.csv │ │ │ ├── 8.csv │ │ │ └── 9.csv │ ├── long_distance │ │ └── test │ │ │ ├── 1.csv │ │ │ ├── 10.csv │ │ │ ├── 2.csv │ │ │ ├── 3.csv │ │ │ ├── 4.csv │ │ │ ├── 5.csv │ │ │ ├── 6.csv │ │ │ ├── 7.csv │ │ │ ├── 8.csv │ │ │ └── 9.csv │ └── short_distance │ │ ├── test │ │ ├── 16.csv │ │ ├── 17.csv │ │ ├── 18.csv │ │ ├── 19.csv │ │ ├── 20.csv │ │ ├── 21.csv │ │ ├── 22.csv │ │ ├── 23.csv │ │ ├── 24.csv │ │ ├── 25.csv │ │ ├── 26.csv │ │ ├── 27.csv │ │ ├── 28.csv │ │ ├── 29.csv │ │ └── 30.csv │ │ └── train │ │ ├── 1.csv │ │ ├── 10.csv │ │ ├── 11.csv │ │ ├── 12.csv │ │ ├── 13.csv │ │ ├── 14.csv │ │ ├── 15.csv │ │ ├── 2.csv │ │ ├── 3.csv │ │ ├── 4.csv │ │ ├── 5.csv │ │ ├── 6.csv │ │ ├── 7.csv │ │ ├── 8.csv │ │ └── 9.csv └── s8 │ ├── line_dataset │ ├── 1.csv │ ├── 11.csv │ ├── 13.csv │ ├── 14.csv │ ├── 16.csv │ ├── 17.csv │ ├── 18.csv │ ├── 19.csv │ ├── 2.csv │ ├── 20.csv │ ├── 21.csv │ ├── 24.csv │ ├── 25.csv │ ├── 26.csv │ ├── 27.csv │ ├── 28.csv │ ├── 29.csv │ ├── 3.csv │ ├── 30.csv │ ├── 5.csv │ ├── 6.csv │ ├── 7.csv │ ├── 8.csv │ └── 9.csv │ ├── long_distance │ └── test │ │ ├── 1.csv │ │ ├── 10.csv │ │ ├── 2.csv │ │ ├── 3.csv │ │ ├── 4.csv │ │ ├── 5.csv │ │ ├── 6.csv │ │ ├── 7.csv │ │ ├── 8.csv │ │ └── 9.csv │ ├── short_distance │ ├── test │ │ ├── 10.csv │ │ ├── 13.csv │ │ ├── 2.csv │ │ ├── 22.csv │ │ ├── 24.csv │ │ ├── 26.csv │ │ ├── 29.csv │ │ ├── 3.csv │ │ ├── 33.csv │ │ ├── 4.csv │ │ └── 9.csv │ └── train │ │ ├── 14.csv │ │ ├── 16.csv │ │ ├── 17.csv │ │ ├── 18.csv │ │ ├── 19.csv │ │ ├── 20.csv │ │ ├── 21.csv │ │ ├── 23.csv │ │ ├── 25.csv │ │ ├── 32.csv │ │ ├── 6.csv │ │ └── 7.csv │ └── short_distance_1.5p2p │ ├── test │ ├── 12.csv │ ├── 15.csv │ ├── 16.csv │ ├── 18.csv │ ├── 2.csv │ ├── 20.csv │ ├── 21.csv │ ├── 22.csv │ ├── 28.csv │ ├── 4.csv │ ├── 5.csv │ ├── 7.csv │ └── 8.csv │ └── train │ ├── 10.csv │ ├── 13.csv │ ├── 24.csv │ ├── 25.csv │ ├── 27.csv │ ├── 29.csv │ ├── 30.csv │ ├── 32.csv │ ├── 33.csv │ ├── 34.csv │ ├── 35.csv │ ├── 6.csv │ └── 9.csv └── figures ├── MoRPI_scheme.jpg ├── RC_car_2_phones.jpg ├── SF_long.png └── gyro_long.png /README.md: -------------------------------------------------------------------------------- 1 |

2 | RC Car 3 |

4 | 5 | # MoRPI: Mobile Robot Pure Inertial Navigation 6 | 7 | ## Introduction 8 | 9 | Mobile robots are used in industrial, leisure, and military applications. In some situations, a robot navigation solution relies only on inertial sensors 10 | and as a consequence, the navigation solution drifts in time. 11 | We propose the MoRPI framework, a mobile robot pure inertial approach. Instead of travelling in a straight line trajectory, the robot moves 12 | in a periodic motion trajectory to enable peak-to-peak estimation. In this manner, instead of performing three integrations to calculate the robot position 13 | in a classical inertial solution, an empirical formula is used to estimate the travelled distance. Two types of MoRPI approaches are suggested, where one is 14 | based on both accelerometer and gyroscope readings while the other is only on gyroscopes. Closed form analytical solutions are derived to show that MoRPI 15 | produces lower position error compared to the classical pure inertial solution. In addition, to evaluate the proposed approach, field experiments were made 16 | with a mobile robot equipped with two types of inertial sensors. In total, 143 trajectories with a time duration of 75 minutes were collected and evaluated. 17 | The results show the benefits of using our approach. 18 | 19 | ## Dataset 20 | 21 | A remote control car and a smartphone were used to perform the experiments and record the inertial data to create our dataset. The smartphone was rigidly 22 | attached to the car. The model of the RC car we used is a STORM Electric 4WD Climbing car. The car dimensions are 385x260x205mm with a wheelbase of 253mm 23 | and tire diameter of 110mm. The car has a realistic suspension system that enables it to reach up to 40 kph and cross rough terrain. 24 | Two different smartphones, with different inertial sensors, were used in our experiments: 25 | 1. A Samsung Galaxy S8 Smartphone with an IMU model of LSM6DSL manufactured by STMicroelectronics. 26 | 2. A Samsung Galaxy S6 smartphone with an IMU model of MPU-6500 manufactured by TDK InvenSense. 27 | In both smartphones, the inertial sensor readings were recorded with a sampling rate of 100Hz. 28 | 29 | Five types of trajectories were made during the field experiments: Straight Line, Periodic Motion - Short Route, Periodic Motion - Long Route, L-Shaped - Straight Lines 30 | and L-Shaped - Periodic Motion. 31 | One hundred and three experiments were made indoors on a floor, while 40 experiments were recorded outdoors on an asphalt surface. The dataset of the periodic 32 | movement was split to have a variety of velocities in both train and test sets, where the train was used to determine the gain, and the test to examine our method. 33 | The groups were divided almost equally. 34 | 35 | ## Algorithm 36 | 37 | MoRPI Scheme 38 | 39 | Both of Our MoRPI approaches consist of the following phases: 40 | 1. **Peak detection**: The peaks during the motion are extracted as local maxima from the inertial measurements. 41 | 2. **Gain calculation**: Prior to the application of the proposed approach, the empirical gain is estimated by moving the robot at a known distance with a known 42 | number of periods while using the Weinberg approach. Once obtained, this gain is used in real-time to estimate the peak-to-peak distance. 43 | 3. **Peak-to-peak distance estimation**: The 'step', in analogue to PDR, is the segment between two peaks. The peak-to-peak distance estimation is done using the 44 | Weinberg approach with the predefined gain and the inertial sensor readings. 45 | 4. **Heading determination**: We use the heading extracted from the transfer matrix to project the peak-to-peak distance into local planar coordinates. 46 | 5. **Position update**: As a dead-reckoning method, the position is updated relative to the previous step while using the current heading angle and peak-to-peak 47 | distance. 48 | 49 | ## Citation 50 | 51 | If you found the experimental DATA useful or used our algorithm for your research, please cite our paper: 52 | ``` 53 | @article{etzion2022morpi, 54 | title={MoRPI: Mobile Robot Pure Inertial Navigation}, 55 | author = {Etzion, Aviad and Klein, Itzik}, 56 | journal={IEEE Journal of Indoor and Seamless Positioning and Navigation}, 57 | url = {https://ieeexplore.ieee.org/document/10323471}, 58 | year={2022}, 59 | } 60 | ``` 61 | 62 | [](https://arxiv.org/abs/2207.02982) 63 | 64 |

65 | 66 |

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